An in vitro method for the prognosis of progression of a cancer and of the outcome in a patient and means for performing said method

European Patent Application EP1777523

The present invention relates to the prognosis of the outcome of a cancer in a patient, which prognosis is based on the quantification of one or several biological markers that are indicative of the presence of, or alternatively the level of, the adaptive immune response of said patient against said cancer.

Galon, Jérome (6 Rue Plumet, 75015 Paris, FR)
Pages, Franck (11 Rue de Silly, 92100 Boulogne-Billancourt, FR)
Fridman, Wolf-herman (27 Rue de Bertholet, 75005 Paris, FR)

EP20050292200

04/25/2007

10/19/2005

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INSERM (Institut National de la Santé et de la Recherche Médicale) (101 rue de Tolbiac, 75654 Paris Cedex 13, FR)

G01N33/574

5631169				Fluorescent energy transfer immunoassay
4868103				Analyte detection by means of energy transfer
4843155				Product and process for isolating RNA
4683202				Process for amplifying nucleic acid sequences
5854033				Rolling circle replication reporter systems
6892141				Primer design system
20050089862				Multiplex real-time quantitative pcr

Catherine, Alain (Cabinet Harlé & Phélip 7, rue de Madrid, 75008 Paris, FR)

1. An in vitro method for the prognosis of patients for progression of a cancer, which method comprises the following steps : a) quantifying, in a tumor tissue sample from said patient, at least one biological marker indicative of the status of the adaptive immune response of said patient against cancer; and b) comparing the value obtained at step a) for said at least one biological marker with a predetermined reference value for the same biological marker; which predetermined reference value is correlated with a specific prognosis of progression of said cancer.

2. The in vitro method according to claim 1, wherein said tumour tissue sample is selected from the group consisting of (i) a global primary tumor (as a whole), (ii) a tissue sample from the center of the tumor, (iii) a tissue sample from the tissue directly surrounding the tumor which tissue may be more specifically named the "invasive margin" of the tumor, (iv) the lymph nodes located at the closest proximity of the tumor, (v) a tumor biopsie performed prior surgery, and (vi) a distant metastasis.

3. The in vitro method according to claim 1, wherein said at least one biological marker is quantified, at step a), in tumor samples collected from two regions of the tumor, respectively (i) the center of the tumor (CT) and (ii) the invasive margin of the tumor (IM).

4. The in vitro method according to claim 1, wherein said at least one biological marker indicative of the status of the adaptive immune response of said patient against cancer consists of at least one biological marker expressed by a cell from the immune system.

5. The in vitro method according to claim 1, wherein said at least one biological marker indicative of the status of the adaptive immune response of said patient against cancer consists of at least one biological marker expressed by a cell from the immune system selected from the group consisting of B lymphocytes, T lymphocytes, dendritic cells, NK cells, NKT cells, and NK-DC cells.

6. The in vitro method according to any one of claims 1 to 5, wherein said at least one biological marker is selected from the group consisting of: (i) a cell subset from the immune system; (ii) a protein expressed by cells from the immune system; (iii) a nucleic acid encoding a protein expressed by cells from the immune system

7. The in vitro method according to claim 1, wherein said at least one biological marker consists of the density of T lymphocytes present at the tumour site.

8. The in vitro method according to claim 1, wherein said at least one biological marker consists of the quantification value of a protein expressed by cells from the immune system present at the tumour site.

9. The in vitro method according to claim 1, wherein said at least one biological marker consists of the quantification value of the expression of gene specifically expressed by cells from the immune system present at the tumour site.

10. The in vitro method according to claim 1, wherein said at least one biological marker is selected from the group consisting of CD3, CD8, GZMB, CD45RO, GLNY, TBX21, IRF1, IFNG, CXCL9, CXCL10, CD4, CXCR3, CXCR6, IL-18, IL-18Rbeta, Fractalkine, IL-23, IL-31, IL-15, IL-7, MIG, Perforin, TCRαβ, TCRγδ, LAT, ZAP70, CD5 and CD2.

11. The in vitro method according to claim 1, wherein said at least one biological marker is selected from the group consisting of the following biological markers : (i) Various biological markers
ICAM-2/CD102, 4-1BB/TNFRSF9, IFN-gamma R1, IFN-gamma R2, B7-1/CD80, IL-1 RI, IL-2 R alpha, BLAME/SLAMF8, IL-2 R beta, CCR3, CCR4, CCR5, CCR6, CCR7, CCR8, IL-7 R alpha, CCR9, CXCR1/IL-8 RA, CD2, CD3epsilon, CD3zeta, CD3gamma, CD4, CD4+/45RA-, IL-12 R beta 1, CD4+/45RO-, IL-12 R beta 2, CD4+/CD62L-/CD44, CD4+/CD62L+/CD44IL-17, CD5, Integrin alpha 4/CD49d, CD6, Integrin alpha E/CD103, CD8, Integrin alpha M/CD11b, CD8+/45RA-, Integrin alpha X/CD11c, CD8+/45RO-, Integrin beta 2/CD18, CD27/TNFRSF7, LAG-3, CD28, LAIR1, CD30/TNFRSF8, LAIR2, CD31/PECAM-1, CD40 Ligand/TNFSF5, NCAM-L1, CD43, NTB-A/SLAMF6, CD45, CD83, CD84/SLAMF5, RANK/TNFRSF11A, L-Selectin, CD229/SLAMF3, SIRP beta 1, CD69, SLAM, Common gamma Chain/IL-2 R gamma, CRACC/SLAMF7, CX3CR1, CXCR3, CXCR4, CXCR6, TNF RI/TNFRSF1A, TNF RII/TNFRSF1B, Fas/TNFRSF6, Fas Ligand/TNFSF6, TSLP, TSLP R, ICAM-1/CD54, IL-2, IFN-gamma, IL-4, IL-5, IL-10, IL-13, (ii) Biological markers of Th 1/Th2 cells:
II-2R Common beta Chain, Common gamma Chain/IL-2 R gamma, IFN-gamma, IFN-gamma R1, IL-12, IFN-gamma R2, IL-12 R beta 1, IL-2, IL-12 R beta 2, IL-2 R alpha, IL-2 R beta, IL-24, TNF RI/TNFRSF1A, TNF RII/TNFRSF1B, IL-4 R, TNF-beta/TNFSF1B, (iii) Biological markers of the Interferon familly:
IFN alpha, IFN beta, IFN-alpha/beta R1, IFN-alpha/beta R2, IFN-gamma R1, IFN-gamma R2, IFN-alpha A, IFN-alpha/beta R2, IFN-alpha B2, IFN-beta, IFN-alpha C, IFN-gamma, IFN-alpha D, IFN-alpha G, IFN-omega, IFN-alpha H2, (iv) Biological markers of the Common gamma Chain Receptor Family :
Common gamma Chain/IL-2 R gamma, IL-7 R alpha, IL-2, IL-9, IL-2 R alpha, IL-9 R, IL-2 R beta, IL-15, IL-15 R alpha, IL-21, IL-7, IL-21 R, IL-31, (v) Biological markers of the CX3C Chemokines & Receptors :
CX3C Chemokine Ligands, CX3CL1/Fractalkine,
CX3C Chemokine receptors,CX3CR1, (vi) Biological markers of CXC Chemokines & Receptors,
CXC Chemokine Ligands, CXCL13/BLC/BCA-1, CXCL11/I-TAC, CXCL14/BRAK, CXCL8/IL-8, CINC-1, CXCL10/IP-10/CRG-2, CINC-2, CINC-3, CXCL16, CXCL15/Lungkine, CXCL5/ENA, CXCL9/MIG, CXCL6/GCP-2, CXCL7/NAP-2, GRO, CXCL4/PF4, CXCL1/GRO alpha, CXCL12/SDF-1, CXCL2/GRO beta, Thymus Chemokine-1, CXCL3/GRO gamma,
CXC Chemokine Receptors, CXCR6, CXCR3, CXCR1/IL-8 RA, CXCR4, CXCR2/IL-8 RB, CXCR5, (vii) Biological markers of CC Chemokines & Receptors,
CC Chemokine Ligands, CCL21/6Ckine, CCL12/MCP-5, CCL6/C10, CCL22/MDC, CCL28, CCL3L1/MIP-1 alpha Isoform LD78 beta, CCL27/CTACK, CCL3/MIP-1 alpha, CCL24/Eotaxin-2, CCL4/MIP-1 beta, CCL26/Eotaxin-3, CCL15/MIP-1 delta, CCL11/Eotaxin, CCL9/10/MIP-1 gamma, CCL14a/HCC-1, MIP-2, CCL14b/HCC-3, CCL19/MIP-3 beta, CCL16/HCC-4, CCL20/MIP-3 alpha, CCL1/I-309/TCA-3, CCL23/MPIF-1, MCK-2, CCL18/PARC, CCL2/MCP-1 , CCL5/RANTES, CCL8/MCP-2, CCL17/TARC, CCL7/MCP-3/MARC, CCL25/TECK, CCL13/MCP-4CC Chemokine Receptors, CCR1, CCR7, CCR2, CCR8, CCR3, CCR9, CCR4, D6, CCR5, HCR/CRAM-A/B, CCR6 (viii) Biological markers of CC Chemokine Inhibitors
CCI, CC Viral Chemokine Homologs, MCV-type II, MIP-II, MIP-I, MIP-III (ix) Biological markers of C Chemokines & Receptors
The C (gamma) subfamily lacks the first and third cysteine residues. Lymphotactin (also known as SCM-1 alpha) and SCM-1 beta are currently the only two family members. Both have chemotactic activity for lymphocytes and NK cells.
C Chemokine Ligands, XCL1/Lymphotactin
C Chemokine Receptors, XCR1
and (x) Biological markers of other Interleukins
I L-12, I L-12 R beta 1, I L-12 R beta 2, IL-27, I L-15, IL-31

12. A kit for the prognosis of progression of a cancer in a patient, which kit comprises means for quantifying at least one biological marker indicative of the status of the adaptive immune response of said patient against cancer.

13. A kit for monitoring the effectiveness of treatment (adjuvant or neo-adjuvant) of a subject with an agent, which kit comprises means for quantifying at least one biological marker indicative of the status of the adaptive immune response of said patient against cancer.

FIELD OF THE INVENTION

The present invention relates to the field of prognosis of the outcome of a cancer in a patient.

More precisely, this invention relates to the prognosis of the outcome of a cancer in a patient, which prognosis is based on the quantification of one or several biological markers that are indicative of the presence of, or alternatively the level of, the adaptive immune response of said patient against said cancer.

BACKGROUND OF THE INVENTION

Because cancer is the second leading cause of death, particularly in Europe and in the United States, vast amount of efforts and financial resources are being invested in developing novel therapeutical approaches. However, the need for reliable diagnostic and prognostic tools is a rate-limiting step in the successful application of a cancer therapy. This is best manifested by the fact that most of the currently known markers of cancer are poorly reliable.

To date, malignant tumors are generally classified according to the TNM system. The TNM (for "Tumor-Node-Metastasis") classification system uses the size of the tumor, the presence or absence of tumor in regional lymph nodes, and the presence or absence of distant metastases, to assign a stage to the tumor ( AJCC Cancer Staging Manual, Lippincott, 5th edition, pp. 171-180, 1997 ). The assigned stage is used as a basis for selection of appropriate therapy and for prognostic purposes. When applied for staging colorectal cancers, the TNM system allows the distinction between (T) the degree of invasion of the intestinal wall, ranging from T0 to T4, (N) the degree of lymph node involvement, ranging from N0 to N3 and (M) the degree of metastasis, ranging from M0 to M1.

For colorectal cancers, a stage may be assigned to the tumor also according to the Duke's classification. Duke's classification allows the distinction between at least four main tumor stages, respectively (A) tumor confined to the bowel wall, (B) tumor extending across the bowel wall, (C) involvement of regional nodes and (D) occurrence of distant metastases.

However, the above clinical classifications, although they are useful, are imperfect and do not allow a reliable prognosis of the outcome of the cancers. This is particularly true for the cancers assigned as Duke Class B, which are of a wide range of seriousness.

Instead of conventional clinical staging, it has been provided in the art a large number of biological markers, including genes and proteins, that would be potentially useful for the diagnosis or the prognosis of a wide variety of cancers. Notably, it has been disclosed various methods for providing patterns of gene expression that would be potentially useful as cancer diagnosis or prognosis tools, including for diagnosis or prognosis of colorectal cancers. However, most of the methods of this kind that are disclosed in the art have not proved their clinical reliability regarding their independent prognosis value, in terms of probability of recurrence or of the expected disease-free survival time (DFS) or of the overall survival (OS).

There is thus a need in the art for improved methods of prognosis of the outcome of cancers, including colorectal cancers, that would stage the disease in a more accurate and a more reliable way than the presently available methods.

Notably, the availability of improved prognosis methods would allow a better selection of patients for appropriate therapeutical treatments, including before and after surgery. Indeed, for numerous cancers including colorectal cancers, the selection of an appropriate therapeutical treatment after surgery is guided by the histopathological data provided by the analysis of the resected tumor tissue. Illustratively, for colorectal cancers, adjuvant chemotherapy treatments are prescribed mostly when involvement of lymph nodes is diagnosed, because of the toxicity of such treatment and its lack of benefit for the other patients.

SUMMARY OF THE INVENTION

[This section will be fulfilled, once an agreement with the inventors will be reached on the final set of claims]

DESCRIPTION OF THE FIGURES

• Figure 1 illustrates results of expression of inflammatory-, immune suppressive- and immune adaptive-related genes in a series of 75 colorectal cancers according to the VELIPI status and relapse. Relative mRNA expression levels were normalized to the level of 18S mRNA for each sample. The levels are represented as fold increase (%) compared to the reference group of invasion positive (VELIPI+) patients that experienced a relapse. **: P<0.05 compared to the reference group.
• Figure 2 shows the mean ± SEM of CD45RO+ cells/mm ² in the different groups of patients (N and M stages, according to the AJCC/UICC TNM staging, na: not applicable). Statistical analyses were performed using Mann-Whitney test. * Represent significant differences (P<0.05).
• Figure 3 shows Kaplan-Meier overall survival (OS) and disease free survival (DFS) curves in CD45RO-hi (>250 CD45RO+ cells/mm ² , n=176, upper line), and in CD45RO-lo (<250 CD45RO+ cells/mm ² , n=160, bottom line).
• Figure 4 shows a comparison of immune population densities in the center (CT) and the invasive margin (IM) of the tumours from patients with- (black histogram) or without- relapse (white histogram).
• Figure 5 The significance of all the cut-off were plotted as a function of the number of CD3 _CT /mm ² (black), and CD3 _IM /mm ² (green) for overall survival. Cut-offs for 25, 50 and 75% of the cohort are also represented for both regions. P-values above the horizontal line (P=0.05) are all significant.
• Figure 6 shows the Median DFS of patients with high- (bottom histograms) or low-densities (upper histograms) of adaptive immune cells in each tumour region (CT or IM) is represented.
• Figure 7 shows Kaplan-Meier curves for the duration of DFS (7A) and OS (7B) according to the presence of High-CD3 density in the center of the tumour (CD3 _CT ^Hi ) and High-CD3 density in the invasive margin (CD3 _IM ^Hi ) (thick-grey), High-CD3 _CT and Low-CD3 _IM (thin-grey), Low-CD3 _CT and High-CD3 _IM (thin-black), and Low-CD3 _CT and Low-CD3 _IM (thick-black), in 415 patients with colorectal cancer (logrank statistical test, P <10 ^-4 for OS and DFS; ** P<10 ^-4 , * P<0.05). Figure 7C shows the combined tumour regions analysis of adaptive immune markers. Median DFS of patients with high- (bottom histograms) or low-densities (upper histograms) of adaptive immune cells in both tumour regions (CT plus IM) is represented.
• Figure 8
  (8a) : Kaplan-Meier curves for the duration of DFS according to the Dukes stages (Dukes A: red (n=75), B: green (n=137), C: blue (n=99), and D: black line (n=95)) in 415 patients with colorectal cancer.
  (8b) : Kaplan-Meier curves for the duration of DFS according to the Dukes stages (as in 8a) and to the presence of Low-CD3 _CT plus Low-CD3 _IM (thick lines, n=93) or High-CD3 _CT plus High-CD3 _IM (thin lines, n=109).
  (8c) : Kaplan-Meier curves for the duration of DFS according to the Dukes stages and to the presence of Low-CD3 _CT plus Low-CD3 _IM plus Low-CD45RO _CT plus Low-CD45RO _IM (thick lines, n=25) or High-CD3 _CT plus High-CD3 _IM plus High-CD45RO _CT plus High-CD45RO _IM (thin lines, n=87). ** P <10 ^-4 .
  (8d) : Kaplan-Meier curves for the duration of OS according to the Dukes stages and to the presence of Low-CD3 _CT plus Low-CD3 _IM plus Low-CD45RO _CT plus Low-CD45RO _IM (thick lines, n=25) or High-CD3 _CT plus High-CD3 _IM plus High-CD45RO _CT plus High-CD45RO _IM (thin lines, n=87).

DETAILED DESCRIPTION OF THE INVENTION

The present invention provides a novel method for the prognosis of the outcome of a cancer in a patient, which novel method is based on the detection and/or the quantification, at the tumor site, of one or more biological markers indicative of the presence of, or alternatively of the level of, the adaptive immune response of said patient against said cancer.

It has now been surprisingly shown according to the invention that the determination of the in situ adaptive immune response to malignant cancers, and especially to colorectal cancers, allows to predict the subsequent clinical outcome, regardless of the extent of local tumor invasion and spread to regional lymph nodes.

This statistically highly significant correlation between (i) the level of the adaptive immune response from the patient at the tumor site and (ii) the outcome of the disease is all the more surprising that, according to the prior art knowledge, the presence of infiltrating immune cells in mammal cancers accounted for highly variable outcomes, ranging from deleterious inflammatory processes to beneficial adaptive immune responses.

Firstly, it has been found according to the invention that there is a high correlation between a high density of T cells at the tumor site and a favorable outcome of the disease. Particularly, it has been shown that a positive outcome of the cancer is highly correlated with the quantification of a high density of CD3+ cells, CD8+ cells, CD45RO+ cells or Granzyme-B+ cells at the site of the tumor, either in the central part of the tumor or in the invasive margin thereof.

Secondly, it has been found that the determination of the presence of high densities of CD3+ cells, CD8+ cells, CD45RO+ cells or Granzyme-B+ cells at the site of the tumor is highly correlated with reduced cancer recurrence and/or delayed cancer recurrence and/or a lack of cancer recurrence.

Thirdly, it has been found that the determination of the presence of high densities of CD3+ cells, CD8+ cells, CD45RO+ cells or Granzyme-B+ cells at the site of the tumor is highly correlated with reduced concommittant distant metastasis, or a lack of concommittant distant metastasis (M-stages).

Fourthly, it has been found that the determination of the presence of high densities of CD3+ cells, CD8+ cells, CD45RO+ cells or Granzyme-B+ cells at the site of the tumor is highly correlated with reduced early metastasis, or a lack of early metastasis (VE or Ll or PI).

Fifthly, it has been found that the determination of the presence of high densities of CD3+ cells, CD8+ cells, CD45RO+ cells or Granzyme-B+ cells at the site of the tumor is highly correlated with a reduced invasion of the regional lymph nodes with tumor cells (N-stages).

Sixthly, it has been found that the determination of the presence of high densities of CD3+ cells, CD8+ cells, CD45RO+ cells or Granzyme-B+ cells at the site of the tumor is highly correlated with a reduced invasion through the intestinal wall (T-stages).

More generally, it has been found that the absence of early dissemination of tumor manifested by tumor emboli in lymphovascular and perineural structures is markedly associated with the presence of a strong in situ immune response, said strong immune response being illustrated, notably, by the high immune cell densities found at the tumor site, as well as by the high expression level of various genes associated with immunity at the tumor site.

Further, it has been found according to the invention that the detection of a strong adaptive immune response at two distinct regions of the tumor, the center of the tumor (CT) plus the invasive margin of the tumor (IM), was highly correlated with a long disease-free survival time and overall survivival time of the patients, and significantly more informative for prognosis of patient of progression of cancer.

Further, it has been found according to the invention that the detection of a strong adaptive immune response at the tumor site was highly correlated with a long disease-free survival time (DFS) and overall survivival time (OS) of the patients.

Thus, a first object of the present invention consists of an in vitro method for the prognosis of progression of a cancer in a patient, which method comprises the following steps :

1. a) quantifying, in a tumor tissue sample from said patient, at least one biological marker indicative of the status of the adaptive immune response of said patient against cancer; and
2. b) comparing the value obtained at step a) for said at least one biological marker with a predetermined reference value for the same biological marker; which predetermined reference value is correlated with a specific prognosis of progression of said cancer.

Unexpectedly, it has been found according to the invention that a strong coordinated adaptive immune response correlated with an equally favourable cancer prognosis.

Still unexpectedly, it has been found that said correlation found according to the invention was independent of the tumor invasion through the intestinal wall and extension to the local lymph-nodes (Duke's classification A, B, C).

Conversely, it has been surprisingly found that a weak in situ adaptive immune response correlated with a very poor prognosis, even in patients with minimal tumor invasion (Duke's classsification A).

Thus, the criteria used according to the cancer prognosis method of he invention, namely the status of the adaptive immune response of the cancer patient, appear not only different from those of the T, N, M and Duke's classification, but are also more precise in predicting disease (disease-free interval and survival time).

Performing the cancer prognosis method of the invention may also indicate, with more precision than the prior art methods, those patients at high-risk of tumor recurrence who may benefit from adjuvant therapy, including immunotherapy.

As intended herein, the expression "prognosis of progression of a cancer" encompasses the prognosis, in a patient wherein the occurrence of a cancer has already been diagnosed, of various events, including:

1. (i) the chances of occurrence of metastasis;
2. (ii) the chances of occurrence of loco-regional recurrence of cancer, including colorectal cancer; and
3. (iii) the chances of occurrence of a long disease-free (DFS) and/or long overall survival (OS) times.

As intended herein, a "tumor tissue sample" encompasses (i) a global primary tumor (as a whole), (ii) a tissue sample from the center of the tumor, (iii) a tissue sample from the tissue directly surrounding the tumor which tissue may be more specifically named the "invasive margin" of the tumor, (iv) the lymph nodes located at the closest proximity of the tumor, (v) a tumor tissue sample collected prior surgery (for follow-up of patients after treatment for exemple), and (vi) a distant metastasis

A tumor tissue sample, irrespective of whether it is derived from the center of the tumor, from the invasive margin of the tumor, or from the closest lymph nodes, encompasses pieces or slices of tissue that have been removed from the tumor center of from the invasive margin surrounding the tumor for further quantification of one or several biological markers, notably through histology or immunohistochemistry methods, through flow cytometry methods and through methods of gene or protein expression analysis, including genomic and proteomic analysis. It will be appreciated that tumor tissue samples may be used in the cancer prognosis method of the present invention. In these embodiments, the level of expression of the biological marker can be assessed by assessing the amount (e.g. absolute amount or concentration) of the biological marker in a tumor tissue sample, e.g., tumor tissue smear obtained from a patient. The cell sample can, of course, be subjected to a variety of well-known post-collection preparative and storage techniques (e.g., nucleic acid and/or protein extraction, fixation, storage, freezing, ultrafiltration, concentration, evaporation, centrifugation, etc.) prior to assessing the amount of the biological marker in the sample. Likewise, tumor tissue smears may also be subjected to post-collection preparative and storage techniques, e.g., fixation.

As intended herein, the "adaptive immune response" encompasses the presence or the activity, including the activation level, of cells from the immune system of the host cancer patient locally at the tumor site.

As intended herein, the expression "the adaptive immune response of said patient against said tumor" encompasses any adaptive immune response of said patient through direct (TCR-dependent) or indirect (TCR-independent), or both, action towards said cancer.

The adaptive immune response means the specific immune response of the host cancer patient against the tumor and encompasses the presence of, the number of, or alternatively the activity of, cells involved in the specific immune response of the host which includes:

As used herein, the T lymphocytes encompass T helper lymphocytes, including Th1 and Th2 T helper lymphocytes cell subsets.

As used herein, the T lymphocytes also encompass T cytototoxic lymphocytes.

Adaptative immunity

In comparison to innate immunity, acquired (adaptive) immunity develops when the body is exposed to various antigens and builds a defense that is specific to that antigen.

The adaptive immune response is antigen-specific and may take days or longer to develop. Cell types with critical roles in adaptive immunity are antigen-presenting cells including macrophages and dendritic cells. Antigen-dependent stimulation of T cell subtypes, B cell activation and antibody production, and the activation of macrophages and NK cells all play important roles in adaptive immunity. The adaptive immune response also includes the development of immunological memory, a process that continues to develop throughout life and enhances future responses to a given antigen.

Lymphocytes, a special type of white blood cell, contain subgroups, B and T lymphocytes, that are key players in acquired immune responses. B lymphocytes (also called B cells) produce antibodies. Antibodies attach to a specific antigen and make it easier for the phagocytes to destroy the antigen. T lymphocytes (T cells) attack antigens directly, and provide control of the immune response. B cells and T cells develop that are specific for ONE antigen type. When you are exposed to a different antigen, different B cells and T cells are formed.

As lymphocytes develop, they normally learn to recognize the body's own tissues (self) as distinctive from tissues and particles not normally found in your body (non-self). Once B cells and T cells are formed, a few of those cells will multiply and provide "memory" for the immune system. This allows the immune system to respond faster and more efficiently the next time you are exposed to the same antigen, and in many cases will prevent you from getting sick. For example, adaptive immunity accounts for an individual who has had chickenpox for being so-called 'immune' to getting chickenpox again.

Adaptive immune system

The adaptive immune system, also called the acquired immune system, explains the interesting fact that when most mammals survive an initial infection by a pathogen, they are generally immune to further illness caused by that same pathogen. This fact is exploited by modern medicine through the use of vaccines. The adaptive immune system is based on immune cells called leukocytes (or white blood cells) that are produced by stem cells in the bone marrow. The immune system can be divided into two parts. Many species, including mammals, have the following type:

The humoral immune system, which acts against bacteria and viruses in the body liquids (such as blood). Its primary means of action are immunoglobulins, also called antibodies, which are produced by B cells (B means they develop in the bone marrow).

The cellular immune system, which takes care of other cells that are infected by viruses. This is done by T cells, also called T lymphocytes (T means they develop in the thymus). There are two major types of T cells:

Cytotoxic T cells (TC cells) recognize infected cells by using T-cell receptors to probe the surface of other cells. If they recognize an infected cell, they release granzymes to signal that cell to become apoptotic ("commit suicide"), thus killing that cell and any viruses it is in the process of creating.

Helper T cells (TH cells) interact with macrophages (which ingest dangerous material), and also produce cytokines (interleukins) that induce the proliferation of B and T cells.

In addition, there are Regulatory T cells (Treg cells) which are important in regulating cell-mediated immunity.

Cytotoxic T cells : a cytotoxic (or TC) T cell is a T cell (a type of white blood cell) which has on its surface antigen receptors that can bind to fragments of antigens displayed by the Class I MHC molecules of virus infected somatic cells and tumor cells. Once activated by a MHC-antigen complex, TC cells release the protein perforin, which forms pores in the target cell's plasma membrane; this causes ions and water to flow into the target cell, making it expand and eventually lyse. TC also release granzyme, a serine protease, that can enter target cells via the perforin-formed pore and induce apoptosis (cell death). Most TC cells have present on the cell surface the protein CD8, which is attracted to portions of the Class I MHC molecule. This affinity keeps the TC cell and the target cell bound closely together during antigen-specific activation. TC cells with CD8 surface protein are called CD8+ T cells.

Helper (or TH) T cells: a helper (or TH) T cell is a T cell (a type of white blood cell) which has on its surface antigen receptors that can bind to fragments of antigens displayed by the Class II MHC molecules found on professional antigen-presenting cells (APCs). Once bound to an antigen, the TH cell proliferates and differentiates into activated TH cells and memory TH cells. Activated TH cells secrete cytokines, proteins or peptides that stimulate other lymphocytes; the most common is interleukin-2 (IL-2), which is a potent T cell growth factor. Activated, proliferating TH cells can differentiate into two major subtypes of cells, Th1 and Th2 cells. These subtypes are defined on the basis of specific cytokines produced. Th1 cells produce interferon-gamma and interleukin 12, while Th2 cells produce interleukin-4, interleukin-5 and interleukin-13. Memory TH cells are specific to the antigen they first encountered and can be called upon during the secondary immune response. Most TH cells have present on the cell surface the protein CD4, which is attracted to portions of the Class II MHC molecule. This affinity keeps the TH cell and the target cell bound closely together during antigen-specific activation. TH cells with CD4 surface protein are called CD4+ T cells. The decrease in number of CD4+ T cells is the primary mechanism by which HIV causes AIDS.

As used herein the expression "tumor site" means the tumor tissue itself as well as the tissue which is in close contact with the tumor tissue, including the invasive margin of the tumor and the regional lymph nodes that are the most close to the tumor tissue or to the invasive margin of the tumor.

As intended herein, the "status" of the adaptive immune response encompasses (i) the existence of a specific immune response against cancer at the tumor site as well as (ii) the level of said specific immune response.

As intended herein, a "biological marker" consists of any detectable, measurable or quantifiable parameter that is indicative of the status of the adaptive immune response of the cancer patient against the tumor. A marker becomes a "biological marker" for the purpose of carrying out the cancer prognosis method of the invention when a good statistical correlation is found between (i) an increase or a decrease of the quantification value for said marker and (ii) the cancer progression actually observed within patients. For calculating correlation values for each marker tested and thus determining the statistical relevance of said marker as a "biological marker" according to the invention, any one of the statistical method known by the one skilled in the art may be used. Illustratively, statistical methods using Kaplan-Meier curves and/or univariate analysis using the log-rank-test and/or a Cox proportional-hazards model may be used, as it is shown in the examples herein. Any marker for which a P value of less than 0.05 (according to univariate and multivariate analysis (for example, log-rank test and Cox test, respectively) is determined consists of a "biological marker" useable in the cancer prognosis method of the invention.

Biological markers include the presence of, or the number or density of, cells from the immune system at the tumor site.

Biological markers also include the presence of, or the amount of proteins specifically produced by cells from the immune system at the tumor site.

Biological markers also include the presence of, or the amount of, any biological material that is indicative of the expression level of genes related to the raising of a specific immune response of the host, at the tumor site. Thus, biological markers include the presence of, or the amount of, messenger RNA (mRNA) transcribed from genomic DNA encoding proteins which are specifically produced by cells from the immune system, at the tumor site.

Biological markers thus include surface antigens that are specifically expressed by cells from the immune system, including by B lymphocytes, T lymphocytes, dendritic cells, NK cells, NKT cells, and NK-DC cells., that are recruited within the tumor tissue or at its close proximity, including within the invasive margin of the tumor and in the closest lymph nodes, or alternatively mRNA encoding for said surface antigens.

Illustratively, surface antigens of interest used as biological markers include CD3, CD4, CD8 and CD45RO that are expressed by T cells or T cell subsets.

For example, if the expression of the CD3 antigen, or the expression of the mRNA thereof, is used as a biological marker, the quantification of this biological marker, at step a) of the method according to the invention, is indicative of the level of the adaptive immune response of the patient involving all T lymphocytes and NKT cells.

For instance, if the expression of the CD8 antigen, or the expression of the mRNA thereof, is used as a biological marker, the quantification of this biological marker, at step a) of the method according to the invention, is indicative of the level of the adaptive immune response of the patient involving cytotoxic T lymphocytes.

For example, if the expression of the CD45RO antigen, or the expression of the mRNA thereof, is used as a biological marker, the quantification of this biological marker, at step a) of the method according to the invention, is indicative of the level of the adaptive immune response of the patient involving memory T lymphocytes or memory effector T lymphocytes.

Yet illustratively, proteins used as biological markers also include cytolytic proteins specifically produced by cells from the immune system, like perforin, granulysin and also granzyme-B.

At the end of step a) of the method according to the invention, a quantification value is obtained for each of the at least one biological marker that is used.

In certain embodiments of the method, the value obtained at the end of step a) consists of the number or the density of cells of the immune system, or cell subsets thereof, at the tumor site. As used herein, the density of cells of interest may be expressed as the number of these cells of interest that are counted per one unit of surface area of tissue sample, e.g. as the number of these cells of interest that are counted per cm ² of surface area of tissue sample. As used herein, the density of cells of interest may also be expressed as the number of these cells of interest per one volume unit of sample, e.g. as the number of cells of interest per cm ³ of sample. As used herein, the density of cells of interest may also consist of the percentage of a specific cell subset (e.g. CD3+ T cells) per total cells or total cell subpopulation (set at 100%). For example in an embodiment of the method, cells are firstly collected by mechanical dispersion from the tumor tissue sample and cells of interest are then counted by flow cytometry, optionally after labeling, for instance by labeled surface antigen-specific antibodies, before determining cell density.

In certain other embodiments of the method, the value obtained at the end of step a) consists of the expression level of protein(s) specifically produced by cells from the immune system at the tumor site. Said expression level may be expressed as any arbitrary unit that reflects the amount of the protein of interest that has been detected in the tissue sample, such as intensity of a radioactive or of a fluorescence signal emitted by a labeled antibody specifically bound to the protein of interest. Alternatively, the value obtained at te end of step a) may consist of a concentration of protein(s) of nterest that could be measured by various protein detection methods well known in the art, such as. ELISA, SELDI-TOF, FACS or Western blotting.

Said expression level may also be expressed as any arbitrary unit that reflects the amount of mRNA encoding said protein of interest that has been detected in the tissue sample, such as intensity of a radioactive or of a fluorescence signal emitted by the cDNA material generated by PCR analysis of the mRNA content of the tissue sample, including by Real-time PCR analysis of the mRNA content of the tissue sample.

At step b) of the method, for each biological marker used, the value which is obtained at the end of step a) is compared with a reference value for the same biological marker. Said reference value for the same biological marker is thus predetermined and is already known to be indicative of a reference status of the adaptive immune response of a patient against cancer. Said predetermined reference value for said biological marker is correlated with a good cancer prognosis, or conversely is correlated with a bad cancer prognosis.

Each reference value for each biological marker is predetermined by carrying out a method comprising the steps of :

1. a) providing at least one collection of tumor tissue samples selected from the group consisting of :
   1. i) a collection of tumor tissue samples from cancer patients classified as Tis, or T1, or T2, or T3 or T4 and N0, or N1, or N2, or N3 and M0 or M1, and with no early metastasis (VE or LI or PI) or with early metastasis, having undergone anti-cancer treatment, and subsequently having no cancer relapse or no cancer recurrence after the anti-cancer treatment;
   2. ii) a collection of tumor tissue samples from cancer patients classified as Tis, or T1, or T2, or T3 or T4 and N0, or N1, or N2, or N3 and M0 or M1, and with no early metastasis (VE or LI or PI) or with early metastasis, having undergone anti-cancer treatment, and subsequently having cancer relapses or recurrences after the anti-cancer treatment.
2. b) quantifying, for each tumor tissue sample comprised in a collection of tumor tissue samples provided at step a), the said biological marker, whereby a collection of quantification values for the said biological marker and for the said collection of tumor tissue samples is obtained;
3. c) calculating, from the said collection of quantification values obtained at the end of step b), the mean quantification value for the said biological marker, whereby a predetermined reference value for said biological marker that is correlated with a specific cancer prognosis is obtained.

The "anti-cancer treatment" that is referred to in the definition of step a) above relate to any type of cancer therapy undergone by the cancer patients previously to collecting the tumor tissue samples, including radiotherapy, chemotherapy and surgery, e.g. surgical resection of the tumor.

According to the method for obtaining predetermined reference values above, more than one predetermined reference value may be obtained for a single biological marker. For example, for a single biological marker, the method above allows the determination of at least four predetermined reference values for the same biological marker, respectively one predetermined reference value calculated from the mean quantification value obtained when starting, at step a), with each of the collections (i) and ii of tumor tissue samples that are described above.

Indeed, for performing the cancer prognosis method according to the invention, the availability of a predetermined reference value for more than one biological marker is preferred. Thus, generally, at least one predetermined reference value is determined for a plurality of biological markers indicative of the status of the adaptive immune response against cancer that are encompassed herein, by simply reiterating the method for obtaining predetermined reference values that is described above, for a plurality of the said biological markers.

Illustrative embodiments of the method for obtaining predetermined reference values described above are disclosed in the examples herein.

For instance, in embodiments wherein the biological marker consists of a surface antigen expressed by cells from the immune system, like the CD3 antigen, and wherein at step a) of the cancer prognosis method a flow cytometry analysis of the CD3+ cell density at the tumor site is carried out, the predetermined reference value may consist of the cell density value, including percentage of specific cells (e.g. CD3+) per total cells or total cell subpopulation (set at 100%), that correlates with bad cancer prognosis, e.g. relapses or recurrences, short survival time, etc., or in contrast may consist of the cell density value that correlates with good cancer prognosis, e;g. no early metastasis, no metastasis at all or long disease-free survival time.

In certain embodiments, the reference predetermined value consists of a "cut-off" value, which "cut-off" value consists of a median quantification value for the biological marker of interest that discriminates between bad cancer prognosis and good cancer prognosis. Illustratively, for human colorectal cancer, it has been found that, when using immunohistochemistry analysis of CD3+ cells at the tumor site as the biological marker, the predetermined cut-off reference value was of about 300 CD3+ cells /mm ² for a tumor tissue sample collected from the center of the tumor, and that the predetermined cut-off reference value was of about 600 CD3+ cells/mm ² for a tumor tissue sample collected from the invasion margin.

The optimal cut-off values based on log-rank tests, for CD3, CD8, CD45RO, GZMB cell densities were 370, 80, 80, 30 cells/mm ² in the center of the tumour, respectively, and 640, 300, 190, 60 cells/mm ² in the invasive margin, respectively, as shown in the examples herein.

According to the embodiments above, a bad cancer prognosis is obtained if the quantification value generated for the CD3+ biological marker is less than the predetermined cut-off reference value, when the comparison is carried out at step b) of the method. Conversely, a good cancer prognosis is obtained if the quantification value generated for the CD3+ biological marker is more than the predetermined cut-off reference value, when the comparison is carried out at step b) of the method

Also illustratively, in embodiments wherein the biological marker consists of the expression level of a gene related to the immune response of the human body, the predetermined reference value may consist of the gene expression value that correlates with bad cancer prognosis, e.g. relapses or recurrences, short survival time, etc., or in contrast may consist of the gene expression value that correlates with good cancer prognosis, e.g. no metastasis at all or long disease-free survival time. The gene expression value may be expressed as any arbitrary unit. For instance, the gene expression value may be expressed as the difference (deltaCT) between (i) the amount of the biological marker-specific mRNA and (ii) the amount of an unrelated mRNA, found in the tumor tissue sample, such as for example the ribosomal 18S mRNA. Illustratively, for human colorectal cancer, the difference between (i) the amount of the biological marker-specific mRNA and (ii) the amount of an unrelated mRNA may be arbitrarily assigned to consist of the deltaCT and of the mean of all values from the reference group (e.g. for patients undergoing early steps of metastasis processes (VELIPI) and relapses, set to "100%"). In these embodiments, the quantification value generated for a particular gene-specific mRNA, at step a) of the method, is more than 100%, then a better cancer prognosis than with the predetermined reference value is obtained. For instance, this is shown in the examples herein, when using notably CD8α-specific mRNA, GZM-B-specific mRNA and GLNY-specific mRNA.

In certain embodiments of step a) of the cancer prognosis method according to the invention, the biological marker(s) is (are) quantified separately in one, or more than one, tumor tissue sample from the cancer patient, selected from the group consisting of (i) a global primary tumor (as a whole), (ii) a tissue sample from the center of the tumor, (iii) a tissue sample from the tissue directly surrounding the tumor which tissue may be more specifically named the "invasive margin" of the tumor (iv) the lymph nodes located at the closest proximity of the tumor, (v) a tumor biopsie perform prior surgery (for follow-up of patients after treatment for exemple), and (vi) a distant metastasis. In these embodiments, quantification value that is obtained, at the end of step a), for each of the tumor tissue samples (i), (ii) or (iii), is compared, at step b) of the method, with the corresponding reference values previously determined for each of the tumor tissue samples (i) to (vi), respectively. Obtaining, at step a) of the method, more than one quantification value for each biological marker that is used allows a more accurate final cancer prognosis than when only one quantification value per biological marker is determined.

In other embodiments of the cancer prognosis method according to the invention, quantification values for more than one biological marker are obtained, at step a) of the method. In these embodiments, step b) is carried out by comparing, for each biological marker used, (i) the quantification value obtained at step a) for this biological marker with (ii) the predetermined reference value for the same biological marker.

In further embodiments of the cancer prognosis method according to the invention, step a) is performed by obtaining quantification values for more than one tumor tissue sample for a single biological marker and step a) is performed by obtaining quantification values for more than one biological markers, which quantification values are then compared, at step b), with the corresponding predetermined reference values.

When performing the cancer prognosis method of the invention with more than one biological marker, the number of distinct biological markers that are quantified at step a) are usually of less than 100 distinct markers, and in most embodiments of less than 50 distinct markers.

Advantageously, when high throughput screening of samples is sought, the cancer prognosis method of the invention is performed by using up to 20 distinct biological markers.

The higher number of distinct biological markers are quantified at step a) of the method, the more accurate the final cancer prognosis will be.

However, as it is shown in the examples herein, a reliable cancer prognosis may be obtained when quantifying a single biological marker at step a) of the method, as it is illustrated, for example, with quantification of CD3+, CD8+, CD45RO, GZM-B, GLNY, TBX21, IRF1, IFNG, CXCL9 and CXCL10 biological markers.

The cancer prognosis method of the invention may comprise a further step c) wherein, depending of the biological marker used, either:

• (i) a good prognosis of patient for cancer progression is determined, with no cancer relapse or with no cancer recurrence, when the quantification value obtained at step a) for a specific biological marker is higher than, or lower than, the corresponding predetermined reference value;
• (i) a bad prognosis of patient for cancer progression is determined, with cancer relapse or with cancer recurrence, when the quantification value obtained at step a) for a specific biological marker is higher than, or lower than, the corresponding predetermined reference value;

Usually, for most of the biological markers used herein, the quantification value increases with an increase of the adaptive immune response against cancer. For instance, when the biological marker that is quantified at step a) consists of a protein or a gene specifically expressed by cells of the immune system, the quantification value of said marker increases with the level of the adaptive immune response against cancer of the patient tested. Thus, when performing step b) of the cancer prognosis method of the invention, a good prognosis is determined when the quantification value for a specific biological marker that is obtained at step a) is higher than the corresponding predetermined reference value, notably in embodiments wherein the predetermined reference value consists of a cut-off value. Conversely, a bad prognosis is determined when the quantification value obtained at step a) for a specific biological marker is lower than the corresponding predetermined reference value, notably in embodiments wherein the predetermined reference value consists of a cut-off value.

Thus, in preferred embodiments of the cancer prognosis method according to the invention, the tumor tissue sample that is referred to in step a) is selected from the group consisting of (i) a global primary tumor (as a whole), (ii) a tissue sample from the center of the tumor, (iii) a tissue sample from the tissue directly surrounding the tumor which tissue may be more specifically named the "invasive margin" of the tumor, (iv) the lymph nodes located at the closest proximity of the tumor, (v) a tumor biopsie perform prior surgery (for follow-up of patients after treatment for exemple), and (vi) a distant metastasis

Preferably, the at least one biological marker indicative of the status of the adaptive immune response of said patient against cancer, that is quantified at step a), consists of at least one biological marker expressed by a cell from the immune system selected from the group consisting of B lymphocytes, T lymphocytes, dendritic cells, NK cells, NKT cells, and NK-DC cells..

Preferably, said at least one biological marker, that is quantified at step a), is selected from the group consisting of :

1. (i) the number of cells from the immune system;
2. (ii) a protein expressed by cells from the immune system;
3. (iii) a nucleic acid encoding a protein expressed by cells from the immune system

In certain embodiments of the method, said at least one biological marker consists of the density of T lymphocytes present at the tumor site.

In certain other embodiments, said at least one biological marker consists of the quantification value of a protein expressed by cells from the immune system present at the tumor site.

In further embodiments of the method, said at least one biological marker consists of the quantification value of the expression of gene specifically expressed by cells from the immune system present at the tumor site.

A list of the preferred biological markers that may be used for carrying out the cancer prognosis method of the invention are listed in Table 2. Table 2 contains, for each biological marker that is listed, the accession number to its nucleic acid and amino acid sequences, as available in the GenBank International database.

Although the cancer prognosis method according to the invention has been tested for colorectal cancer, said method may be applied for a wide variety of cancers. Without wishing to be bound by any particular theory, the inventors believe that the cancer prognosis method of the invention may be successfully carried out for prognosing the progression of any cancer that develops from a central tumor to which cells from the immune system have access.

Thus, the cancer prognosis method according to the invention is potentially useful for determining the prognosis of patients for progression of a cancer selected from the group consisting of adrenal cortical cancer, anal cancer, bile duct cancer (e.g. periphilar cancer, distal bile duct cancer, intrahepatic bile duct cancer), bladder cancer, bone cancer (e.g. osteoblastoma, osteochrondroma, hemangioma, chondromyxoid fibroma, osteosarcoma, chondrosarcoma, fibrosarcoma, malignant fibrous histiocytoma, giant cell tumor of the bone, chordoma, lymphoma, multiple myeloma), brain and central nervous system cancer (e.g. meningioma, astocytoma, oligodendrogliomas, ependymoma, gliomas, medulloblastoma, ganglioglioma, Schwannoma, germinoma, craniopharyngioma), breast cancer (e.g. ductal carcinoma in situ, infiltrating ductal carcinoma, infiltrating lobular carcinoma, lobular carcinoma in situ, gynecomastia), Castleman disease (e.g. giant lymph node hyperplasia, angiofollicular lymph node hyperplasia), cervical cancer, colorectal cancer, endometrial cancer (e.g. endometrial adenocarcinoma, adenocanthoma, papillary serous adnocarcinoma, clear cell), esophagus cancer, gallbladder cancer (mucinous adenocarcinoma, small cell carcinoma), gastrointestinal carcinoid tumors (e.g. choriocarcinoma, chorioadenoma destruens), Hodgkin's disease, non-Hodgkin's lymphoma, Kaposi's sarcoma, kidney cancer (e.g. renal cell cancer), laryngeal and hypopharyngeal cancer, liver cancer (e.g. hemangioma, hepatic adenoma, focal nodular hyperplasia, hepatocellular carcinoma), lung cancer (e.g. small cell lung cancer, non-small cell lung cancer), mesothelioma, plasmacytoma, nasal cavity and paranasal sinus cancer (e.g. esthesioneuroblastoma, midline granuloma), nasopharyngeal cancer, neuroblastoma, oral cavity and oropharyngeal cancer, ovarian cancer, pancreatic cancer, penile cancer, pituitary cancer, prostate cancer, retinoblastoma, rhabdomyosarcoma (e.g. embryonal rhabdomyosarcoma, alveolar rhabdomyosarcoma, pleomorphic rhabdomyosarcoma), salivary gland cancer, skin cancer (e.g. melanoma, nonmelanoma skin caner), stomach cancer, testicular cancer (e.g. seminoma, nonseminoma germ cell cancer), thymus cancer, thyroid cancer (e.g. follicular carcinoma, anaplastic carcinoma, poorly differentiated carcinoma, medullary thyroid carcinoma, thyroid lymphoma), vaginal cancer, vulvar cancer, and uterine cancer (e.g. uterine leiomyosarcoma).

In still further embodiments of the method, said at least one biological marker is selected from the group consisting of CD3, CD8, GZMB, CD45RO, GLNY, TBX21, IRF1, IFNG, CXCL9, CXCL10, CD4, CXCR3, CXCR6, IL-18, IL-18Rbeta, Fractalkine, IL-23, IL-31, IL-15, IL-7, MIG, Perforin, TCRαβ, TCRγδ, LAT, ZAP70, CD5 and CD2.

In yet further embodiments of the method, said at least one biological marker is selected from the group consisting of the following biological markers:

• (i) Various biological markers
  ICAM-2/CD102, 4-1BB/TNFRSF9, IFN-gamma R1, IFN-gamma R2, B7-1/CD80, IL-1 RI, IL-2 R alpha, BLAME/SLAMF8, IL-2 R beta, CCR3, CCR4, CCR5, CCR6, CCR7, CCR8, IL-7 R alpha, CCR9, CXCR1/IL-8 RA, CD2, CD3epsilon, CD3zeta, CD3gamma, CD4, CD4+/45RA-, IL-12 R beta 1, CD4+/45RO-, IL-12 R beta 2, CD4+/CD62L-/CD44, CD4+/CD62L+/CD44IL-17, CD5, Integrin alpha 4/CD49d, CD6, Integrin alpha E/CD103, CD8, Integrin alpha M/CD11b, CD8+/45RA-, Integrin alpha X/CD11c, CD8+/45RO-, Integrin beta 2/CD18, CD27/TNFRSF7, LAG-3, CD28, LAIR1, CD30/TNFRSF8, LAIR2, CD31/PECAM-1, CD40 Ligand/TNFSF5, NCAM-L1, CD43, NTB-A/SLAMF6, CD45, CD83, CD84/SLAMF5, RANK/TNFRSF11A, L-Selectin, CD229/SLAMF3, SIRP beta 1, CD69, SLAM, Common gamma Chain/IL-2 R gamma, CRACC/SLAMF7, CX3CR1, CXCR3, CXCR4, CXCR6, TNF RI/TNFRSF1A, TNF RII/TNFRSF1B, Fas/TNFRSF6, Fas Ligand/TNFSF6, TSLP, TSLP R, ICAM-1/CD54, IL-2, IFN-gamma, IL-4, IL-5, IL-10, IL-13,
• (ii) Biological markers of Th1 / Th2 cells :
  II-2R Common beta Chain, Common gamma Chain/IL-2 R gamma, IFN-gamma, IFN-gamma R1, IL-12, IFN-gamma R2, IL-12 R beta 1, IL-2, IL-12 R beta 2, IL-2 R alpha, IL-2 R beta, IL-24, TNF RI/TNFRSF1A, TNF RII/TNFRSF1B, IL-4 R, TNF-beta/TNFSF1B,
• (iii) Biological markers of the Interferon familly :
  IFN alpha, IFN beta, IFN-alpha/beta R1, IFN-alpha/beta R2, IFN-gamma R1, IFN-gamma R2, IFN-alpha A, IFN-alpha/beta R2, IFN-alpha B2, IFN-beta, IFN-alpha C, IFN-gamma, IFN-alpha D, IFN-alpha G, IFN-omega, IFN-alpha H2,
• (iv) Biological markers of the Common gamma Chain Receptor Family:
  Common gamma Chain/IL-2 R gamma, IL-7 R alpha, IL-2, IL-9, IL-2 R alpha, IL-9 R, IL-2 R beta, IL-15, IL-15 R alpha, IL-21, IL-7, IL-21 R, IL-31,
• (v) Biological markers of the CX3C Chemokines & Receptors :
  CX3C Chemokine Ligands, CX3CL1/Fractalkine,
  CX3C Chemokine receptors,CX3CR1,
• (vi) Biological markers of CXC Chemokines & Receptors,
  CXC Chemokine Ligands, CXCL13/BLC/BCA-1, CXCL11/I-TAC, CXCL14/BRAK, CXCL8/IL-8, CINC-1, CXCL10/IP-10/CRG-2, CINC-2, CINC-3, CXCL16, CXCL15/Lungkine, CXCL5/ENA, CXCL9/MIG, CXCL6/GCP-2, CXCL7/NAP-2, GRO, CXCL4/PF4, CXCL1/GRO alpha, CXCL12/SDF-1, CXCL2/GRO beta, Thymus Chemokine-1, CXCL3/GRO gamma,
  CXC Chemokine Receptors, CXCR6, CXCR3, CXCR1/IL-8 RA, CXCR4, CXCR2/IL-8 RB, CXCR5,
• (vii) Biological markers of CC Chemokines & Receptors,
  CC Chemokine Ligands, CCL21/6Ckine, CCL12/MCP-5, CCL6/C10, CCL22/MDC, CCL28, CCL3L1/MIP-1 alpha Isoform LD78 beta, CCL27/CTACK, CCL3/MIP-1 alpha, CCL24/Eotaxin-2, CCL4/MIP-1 beta, CCL26/Eotaxin-3, CCL15/MIP-1 delta, CCL11/Eotaxin, CCL9/10/MIP-1 gamma, CCL14a/HCC-1, MIP-2, CCL14b/HCC-3, CCL19/MIP-3 beta, CCL16/HCC-4, CCL20/MIP-3 alpha, CCL1/I-309/TCA-3, CCL23/MPIF-1, MCK-2, CCL18/PARC, CCL2/MCP-1, CCL5/RANTES, CCL8/MCP-2, CCL17/TARC, CCL7/MCP-3/MARC, CCL25/TECK, CCL13/MCP-4CC Chemokine Receptors, CCR1, CCR7, CCR2, CCR8, CCR3, CCR9, CCR4, D6, CCR5, HCR/CRAM-A/B, CCR6
• (viii) Biological markers of CC Chemokine Inhibitors
  CCI, CC Viral Chemokine Homologs, MCV-type II, MIP-II, MIP-I, MIP-III
• ( ix ) Biological markers of C Chemokines & Receptors
  The C (gamma) subfamily lacks the first and third cysteine residues. Lymphotactin (also known as SCM-1 alpha) and SCM-1 beta are currently the only two family members. Both have chemotactic activity for lymphocytes and NK cells.
  C Chemokine Ligands, XCL1/Lymphotactin
  C Chemokine Receptors, XCR1
• (x) Biological markers of other Interleukins
  IL-12, IL-12 R beta 1, IL-12 R beta 2, IL-27, IL-15, IL-31

In the present specification, the name of each of the various biological markers of interest refers to the internationally recognised name of the corresponding gene, as found in internationally recognised gene sequences and protein sequences databases, including in the database from the HUGO Gene Nomenclature Committee, that is available notably at the following Internet address:

• http://www.gene.ud.ac.uk/nomenciature/index.html

In the present specification, the name of each of the various biological markers of interest may also refer to the internationally recognised name of the corresponding gene, as found in the internationally recognised gene sequences and protein sequences database Genbank.

Through these internationally recognised sequence databases, the nucleic acid and the amino acid sequences corresponding to each of the biological marker of interest described herein may be retrieved by the one skilled in the art.OK In yet further embodiments of the method, as already mentioned above, quantification values for a combination of biological markers are obtained at step a) of the cancer prognosis method of the invention.

Thus, the cancer prognosis method of the invention may be performed with a combination of biological markers. The number of biological markers used is only limited by the number of distinct biological markers of interest that are practically available at the time of carrying out the method. However, a too much high number of biological markers will significantly increase the duration of the method without simultaneously significantly improving the final prognosis determination.

Usually, in the embodiments wherein the cancer prognosis method of the invention is performed with a combination of biological markers, not more than 50 distinct biological markers are quantified at step a). In most embodiments, a combination of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49 and 50 distinct biological markers are quantified.

Illustratively, a combination of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20, 21, 22, 23, 24, 25, 26, 27, or 28 distinct biological markers are quantified at step a), which biological markers are selected from the group consisting of CD3, CD8, GZMB, CD45RO, GLNY, TBX21, IRF1, IFNG, CXCL9, CXCL10, CD4, CXCR3, CXCR6, IL-18, IL-18Rbeta, Fractalkine, IL-23, IL-31, IL-15, IL-7, MIG, Perforin, TCRαβ, TCRγδ, LAT, ZAP70, CD5, CD2.

In certain embodiments of the method, a combination of two biological markers is used at step a), that may be also termed herein a "set" of biological markers.

Illustratively _; the combination of two biological markers may be selected from the group consisting of CD8A-TBX21, CD3Z-CD8A, CD3Z-TBX21, B7H3-TGFB1, IFNG-TBX21, CD4-CD8A, CD8A, IFNG, CD4-TBX21, CD3Z-CD4, CD4-TGFB1, CD8A-GLNY, IFNG-IRF1, GLNY-IFNG, IRF1-TBX21, IL8-PTGS2, GLNY-TBX21, CD3Z-GLNY, CD3Z-IFNG, GZMB-IFNG, GLNY-IRF1, IL10-TGFB1, CD8A-IL 10, CD4-IL10, CD8A-GZMB, GZMB-TBX21, CD3Z-GZMB, CD4-IRF1, GNLY-GZMB, B7H3-IL10, CD4-GZMB, GZMB-IRF1, IL10-TBX21, CD4-IFNG, B7H3-CD4, CDBA-TGFB1, CD3Z-IL10 and CD4-GNLY.

Any combination of two biological markers selected from the group of biological markers that are described in the present specification are herein encompassed by the invention.

General methods for quantifying biological markers

Any one of the methods known by the one skilled in the art for quantifying cellular types, a protein-type or an nucleic acid-type biological marker encompassed herein may be used for performing the cancer prognosis method of the invention. Thus any one of the standard and non-standard (emerging) techniques well known in the art for detecting and quantifying a protein or a nucleic acid in a sample can readily be applied.

Such techniques include detection and quantification of nucleic acid-type biological markers with nucleic probes or primers.

Such techniques also include detection and quantification of protein-type biological markers with any type of ligand molecule that specifically binds thereto, including nucleic acids (e.g. nucleic acids selected for binding through the well known Selex method), and antibodies including antibody fragments. In certain embodiments wherein the biological marker of interest consists of an enzyme, these detection and quantification methods may also include detection and quantification of the corresponding enzyme activity.

Noticeably, antibodies are presently already available for most, if not all, the biological markers described in the present specification, including those biological markers that are listed in Table 2.

Further, in situations wherein no antibody is yet available for a given biological marker, or in situations wherein the production of further antibodies to a given biological marker is sought, then antibodies to said given biological markers may be easily obtained with the conventional techniques, including generation of antibody-producing hybridomas. In this method, a protein or peptide comprising the entirety or a segment of a biological marker protein is synthesized or isolated (e.g. by purification from a cell in which it is expressed or by transcription and translation of a nucleic acid encoding the protein or peptide in vivo or in vitro using known methods). A vertebrate, preferably a mammal such as a mouse, rat, rabbit, or sheep, is immunized using the protein or peptide. The vertebrate may optionally (and preferably) be immunized at least one additional time with the protein or peptide, so that the vertebrate exhibits a robust immune response to the protein or peptide. Splenocytes are isolated from the immunized vertebrate and fused with an immortalized cell line to form hybridomas, using any of a variety of methods well known in the art. Hybridomas formed in this manner are then screened using standard methods to identify one or more hybridomas which produce an antibody which specifically binds with the biological marker protein or a fragment thereof. The invention also encompasses hybridomas made by this method and antibodies made using such hybridomas. Polyclonal antibodies may be used as well.

Expression of a biological marker of the invention may be assessed by any of a wide variety of well known methods for detecting expression of a transcribed nucleic acid or protein. Non-limiting examples of such methods include immunological methods for detection of secreted, cell-surface, cytoplasmic, or nuclear proteins, protein purification methods, protein function or activity assays, nucleic acid hybridization methods, nucleic acid reverse transcription methods, and nucleic acid amplification methods.

In one preferred embodiment, expression of a marker is assessed using an antibody (e.g. a radio-labeled, chromophore-labeled, fluorophore-labeled, polymer-backbone-antibody, or enzyme-labeled antibody), an antibody derivative (e.g. an antibody conjugated with a substrate or with the protein or ligand of a protein-ligand pair {e.g. biotin-streptavidin}), or an antibody fragment (e.g. a single-chain antibody, an isolated antibody hypervariable domain, etc.) which binds specifically with a marker protein or fragment thereof, including a marker protein which has undergone all or a portion of its normal post-translational modification.

In another preferred embodiment, expression of a marker is assessed by preparing mRNA/cDNA (i.e. a transcribed polynucleotide) from cells in a patient tumor tissue sample, and by hybridizing the mRNA/cDNA with a reference polynucleotide which is a complement of a marker nucleic acid, or a fragment thereof. cDNA can, optionally, be amplified using any of a variety of polymerase chain reaction methods prior to hybridization with the reference polynucleotide; preferably, it is not amplified.

In a related embodiment, a mixture of transcribed polynucleotides obtained from the sample is contacted with a substrate having fixed thereto a polynucleotide complementary to or homologous with at least a portion (e.g. at least 7, 10, 15, 20, 25, 30, 40, 50, 100, 500, or more nucleotide residues) of a biological marker nucleic acid. If polynucleotides complementary to or homologous with are differentially detectable on the substrate (e.g. detectable using different chromophores or fluorophores, or fixed to different selected positions), then the levels of expression of a plurality of markers can be assessed simultaneously using a single substrate (e.g. a "gene chip" microarray of polynucleotides fixed at selected positions). When a method of assessing marker expression is used which involves hybridization of one nucleic acid with another, it is preferred that the hybridization be performed under stringent hybridization conditions.

An exemplary method for detecting and/or quantifying a biological marker protein or nucleic acid in a tumor tissue sample sample involves obtaining a tumor tissue sample (e.g. (i) a global primary tumor (as a whole), (ii) a tissue sample from the center of the tumor, (iii) a tissue sample from the tissue directly surrounding the tumor which tissue may be more specifically named the "invasive margin" of the tumor, (iv) the lymph nodes located at the closest proximity of the tumor, (v) a tumor biopsie perform prior surgery (for follow-up of patients after treatment for exemple), and (vi) a distant metastasis, from a cancer patient. Said method includes futher steps of contacting the biological sample with a compound or an agent capable of detecting the polypeptide or nucleic acid (e.g., mRNA, genomic DNA, or cDNA). The detection methods of the invention can thus be used to detect mRNA, protein, _C DNA, or genomic DNA, for example, in a tumor tissue sample in vitro. For example, in vitro techniques for detection of mRNA include Northern hybridizations and in situ hybridizations. In vitro techniques for detection of a biological marker protein include enzyme linked immunosorbent assays (ELISAs), Western blots, immunoprecipitations and immunofluorescence. Furthermore, in vivo techniques for detection of a marker protein include introducing into a subject a labeled antibody directed against the protein or fragment thereof. For example, the antibody can be labeled with a radioactive marker whose presence and location in a subject can be detected by standard imaging techniques.

A general principle of such detection and/or quantification assays involves preparing a sample or reaction mixture that may contain a biological marker, and a probe, under appropriate conditions and for a time sufficient to allow the marker and probe to interact and bind, thus forming a complex that can be removed and/or detected in the reaction mixture.

As used herein, the term "probe" refers to any molecule which is capable of selectively binding to a specifically intended target molecule, for example, a nucleotide transcript or protein encoded by or corresponding to a biological marker. Probes can be either synthesized by one skilled in the art, or derived from appropriate biological preparations. For purposes of detection of the target molecule, probes may be specifically designed to be labeled, as described herein. Examples of molecules that can be utilized as probes include, but are not limited to, RNA, DNA, proteins, antibodies, and organic molecules.

These detection and/or quantification assays of a biological marker can be conducted in a variety of ways.

For example, one method to conduct such an assay would involve anchoring the probe onto a solid phase support, also referred to as a substrate, and detecting target marker/probe complexes anchored on the solid phase at the end of the reaction. In one embodiment of such a method, a sample from a subject, which is to be assayed for quantification of the biological marker, can be anchored onto a carrier or solid phase support. In another embodiment, the reverse situation is possible, in which the probe can be anchored to a solid phase and a sample from a subject can be allowed to react as an unanchored component of the assay.

There are many established methods for anchoring assay components to a solid phase. These include, without limitation, marker or probe molecules which are immobilized through conjugation of biotin and streptavidin. Such biotinylated assay components can be prepared from biotin-NHS (N-hydroxy-succinimide) using techniques known in the art (e.g., biotinylation kit, Pierce Chemicals, Rockford, III.), and immobilized in the wells of streptavidin-coated 96 well plates (Pierce Chemical). In certain embodiments, the surfaces with immobilized assay components can be prepared in advance and stored.

Other suitable carriers or solid phase supports for such assays include any material capable of binding the class of molecule to which the marker or probe belongs. Well-known supports or carriers include, but are not limited to, glass, polystyrene, nylon, polypropylene, nylon, polyethylene, dextran, amylases, natural and modified celluloses, polyacrylamides, gabbros, and magnetite.

In order to conduct assays with the above mentioned approaches, the non-immobilized component is added to the solid phase upon which the second component is anchored. After the reaction is complete, uncomplexed components may be removed (e.g., by washing) under conditions such that any complexes formed will remain immobilized upon the solid phase. The detection of marker/probe complexes anchored to the solid phase can be accomplished in a number of methods outlined herein.

In a preferred embodiment, the probe, when it is the unanchored assay component, can be labeled for the purpose of detection and readout of the assay, either directly or indirectly, with detectable labels discussed herein and which are well-known to one skilled in the art.

It is also possible to directly detect marker/probe complex formation without further manipulation or labeling of either component (marker or probe), for example by utilizing the technique of fluorescence energy transfer (see, for example,

Lakowicz et al., U.S. Pat. No. 5,631,169 ;

Stavrianopoulos, et al., U.S. Pat. No. 4,868,103 ). A fluorophore label on the first, 'donor' molecule is selected such that, upon excitation with incident light of appropriate wavelength, its emitted fluorescent energy will be absorbed by a fluorescent label on a second 'acceptor' molecule, which in turn is able to fluoresce due to the absorbed energy. Alternately, the 'donor' protein molecule may simply utilize the natural fluorescent energy of tryptophan residues. Labels are chosen that emit different wavelengths of light, such that the acceptor molecule label may be differentiated from that of the 'donor'. Since the efficiency of energy transfer between the labels is related to the distance separating the molecules, spatial relationships between the molecules can be assessed. In a situation in which binding occurs between the molecules, the fluorescent emission of the 'acceptor' molecule label in the assay should be maximal. A FRET binding event can be conveniently measured through standard fluorometric detection means well known in the art (e.g., using a fluorimeter).

In another embodiment, determination of the ability of a probe to recognize a marker can be accomplished without labeling either assay component (probe or marker) by utilizing a technology such as real-time Biomolecular Interaction Analysis (BIA) (see, e.g., Sjolander, S. and Urbaniczky, C., 1991, Anal. Chem. 63:2338-2345 and Szabo et al., 1995, Curr. Opin. Struct. Biol. 5:699-705 ). As used herein, "BIA" or "surface plasmon resonance" is a technology for studying biospecific interactions in real time, without labeling any of the interactants (e.g., BIAcore). Changes in the mass at the binding surface (indicative of a binding event) result in alterations of the refractive index of light near the surface (the optical phenomenon of surface plasmon resonance (SPR)), resulting in a detectable signal which can be used as an indication of real-time reactions between biological molecules.

Alternatively, in another embodiment, analogous diagnostic and prognostic assays can be conducted with marker and probe as solutes in a liquid phase. In such an assay, the complexed marker and probe are separated from uncomplexed components by any of a number of standard techniques, including but not limited to: differential centrifugation, chromatography, electrophoresis and immunoprecipitation. In differential centrifugation, marker/probe complexes may be separated from uncomplexed assay components through a series of centrifugal steps, due to the different sedimentation equilibria of complexes based on their different sizes and densities (see, for example, Rivas, G., and Minton, A. P., 1993, Trends Biochem Sci. 18(8):284-7 ). Standard chromatographic techniques may also be utilized to separate complexed molecules from uncomplexed ones. For example, gel filtration chromatography separates molecules based on size, and through the utilization of an appropriate gel filtration resin in a column format, for example, the relatively larger complex may be separated from the relatively smaller uncomplexed components. Similarly, the relatively different charge properties of the marker/probe complex as compared to the uncomplexed components may be exploited to differentiate the complex from uncomplexed components, for example through the utilization of ion-exchange chromatography resins. Such resins and chromatographic techniques are well known to one skilled in the art (see, e.g., Heegaard, N. H., 1998, J. Mol. Recognit. Winter 11(1-6):141-8 ; Hage, D. S., and Tweed, S. A. J Chromatogr B Biomed Sci Appl 1997 Oct. 10;699(1-2):499-525 ). Gel electrophoresis may also be employed to separate complexed assay components from unbound components (see, e.g., Ausubel et al., ed., Current Protocols in Molecular Biology, John Wiley & Sons, New York, 1987-1999 ). In this technique, protein or nucleic acid complexes are separated based on size or charge, for example. In order to maintain the binding interaction during the electrophoretic process, non-denaturing gel matrix materials and conditions in the absence of reducing agent are typically preferred. SELDI-TOF technique may also be employed on matrix or beads coupled with active surface, or not, or antibody coated surface, or beads.

Appropriate conditions to the particular assay and components thereof will be well known to one skilled in the art.

In a particular embodiment, the level of marker mRNA can be determined both by in situ and by in vitro formats in a biological sample using methods known in the art. The term "biological sample" is intended to include tissues, cells, biological fluids and isolates thereof, isolated from a subject, as well as tissues, cells and fluids present within a subject. Many expression detection methods use isolated RNA. For in vitro methods, any RNA isolation technique that does not select against the isolation of mRNA can be utilized for the purification of RNA from colorectal cancer (see, e.g., Ausubel et al., ed., Current Protocols in Molecular Biology, John Wiley & Sons, New York 1987-1999 ). Additionally, large numbers of tissue samples can readily be processed using techniques well known to those of skill in the art, such as, for example, the single-step RNA isolation process of Chomczynski (1989,

U.S. Pat. No. 4,843,155 ).

The isolated mRNA can be used in hybridization or amplification assays that include, but are not limited to, Southern or Northern analyses, polymerase chain reaction analyses and probe arrays. One preferred diagnostic method for the detection of mRNA levels involves contacting the isolated mRNA with a nucleic acid molecule (probe) that can hybridize to the mRNA encoded by the gene being detected. The nucleic acid probe can be, for example, a full-length cDNA, or a portion thereof, such as an oligonucleotide of at least 7, 15, 30, 50, 100, 250 or 500 nucleotides in length and sufficient to specifically hybridize under stringent conditions to a mRNA or genomic DNA encoding a marker of the present invention. Other suitable probes for use in the pronostic assays of the invention are described herein. Hybridization of an mRNA with the probe indicates that the marker in question is being expressed.

In one format, the mRNA is immobilized on a solid surface and contacted with a probe, for example by running the isolated mRNA on an agarose gel and transferring the mRNA from the gel to a membrane, such as nitrocellulose. In an alternative format, the probe(s) are immobilized on a solid surface and the mRNA is contacted with the probe(s), for example, in an Affymetrix gene chip array. A skilled artisan can readily adapt known mRNA detection methods for use in detecting the level of mRNA encoded by the markers of the present invention.

An alternative method for determining the level of mRNA marker in a sample involves the process of nucleic acid amplification, e.g., by rtPCR (the experimental embodiment set forth in

Mullis, 1987, U.S. Pat. No. 4,683,202 ), ligase chain reaction ( Barany, 1991, Proc. Natl. Acad. Sci. USA, 88:189-193 ), self sustained sequence replication ( Guatelli et al., 1990, Proc. Natl. Acad. Sci. USA 87:1874-1878 ), transcriptional amplification system ( Kwoh et al., 1989, Proc. Natl. Acad. Sci. USA 86:1173-1177 ), Q-Beta Replicase ( Lizardi et al., 1988, Bio/Technology 6:1197 ), rolling circle replication (

Lizardi et al., U.S. Pat. No. 5,854,033 ) or any other nucleic acid amplification method, followed by the detection of the amplified molecules using techniques well known to those of skill in the art. These detection schemes are especially useful for the detection of nucleic acid molecules if such molecules are present in very low numbers. As used herein, amplification primers are defined as being a pair of nucleic acid molecules that can anneal to 5' or 3' regions of a gene (plus and minus strands, respectively, or vice-versa) and contain a short region in between. In general, amplification primers are from about 10 to 30 nucleotides in length and flank a region from about 50 to 200 nucleotides in length. Under appropriate conditions and with appropriate reagents, such primers permit the amplification of a nucleic acid molecule comprising the nucleotide sequence flanked by the primers.

For in situ methods, mRNA does not need to be isolated from the colorectal cancer prior to detection. In such methods, a cell or tissue sample is prepared/processed using known histological methods. The sample is then immobilized on a support, typically a glass slide, and then contacted with a probe that can hybridize to mRNA that encodes the marker.

As an alternative to making determinations based on the absolute expression level of the marker, determinations may be based on the normalized expression level of the marker. Expression levels are normalized by correcting the absolute expression level of a marker by comparing its expression to the expression of a gene that is not a marker, e.g., a housekeeping gene that is constitutively expressed. Suitable genes for normalization include housekeeping genes such as the actin gene, ribosomal 18S gene, GAPD gene, or epithelial cell-specific genes. This normalization allows the comparison of the expression level in one sample, e.g., a patient sample, to another sample, e.g., a non-colorectal cancer sample, or between samples from different sources.

Alternatively, the expression level can be provided as a relative expression level. To determine a relative expression level of a marker, the level of expression of the marker is determined for 10 or more samples of normal versus cancer cell isolates, preferably 50 or more samples, prior to the determination of the expression level for the sample in question. The mean expression level of each of the genes assayed in the larger number of samples is determined and this is used as a baseline expression level for the marker. The expression level of the marker determined for the test sample (absolute level of expression) is then divided by the mean expression value obtained for that marker. This provides a relative expression level.

As already mentioned previously in the present specification, one preferred agent for detecting and/or quantifying a biological marker protein when performing the cancer prognosis method of the invention is an antibody that specifically bind to such a biological marker protein or a fragment thereof, preferably an antibody with a detectable label. Antibodies can be polyclonal, or more preferably, monoclonal. An intact antibody, or a fragment or derivative thereof (e.g., Fab or F(ab').sub.2) can be used. The term "labeled", with regard to the probe or antibody, is intended to encompass direct labeling of the probe or antibody by coupling (i.e., physically linking) a detectable substance to the probe or antibody, as well as indirect labeling of the probe or antibody by reactivity with another reagent that is directly labeled. Examples of indirect labeling include detection of a primary antibody using a fluorescently labeled secondary antibody and end-labeling of a DNA probe with biotin such that it can be detected with fluorescently labeled streptavidin.

A variety of formats can be employed to determine whether a sample contains a biological marker protein that binds to a given antibody. Examples of such formats include, but are not limited to, enzyme immunoassay (EIA), radioimmunoassay (RIA), Western blot analysis and enzyme linked immunoabsorbant assay (ELISA). A skilled artisan can readily adapt known protein/antibody detection and/or quantification methods for use in the cancer prognosis method according to the invention.

In one format, antibodies, or antibody fragments or derivatives, can be used in methods such as Western blots, SELDI-TOF (carried out with antibody-beads coupled or matrix) or immunofluorescence techniques to detect the expressed proteins. In such uses, it is generally preferable to immobilize either the antibody or proteins on a solid support. Suitable solid phase supports or carriers include any support capable of binding an antigen or an antibody. Well-known supports or carriers include glass, polystyrene, polypropylene, polyethylene, dextran, nylon, amylases, natural and modified celluloses, polyacrylamides, gabbros, and magnetite.

One skilled in the art will know many other suitable carriers for binding antibody or antigen, and will be able to adapt such support for use with the present invention. For example, protein isolated from colorectal cancer can be run on a polyacrylamide gel electrophoresis and immobilized onto a solid phase support such as nitrocellulose. The support can then be washed with suitable buffers followed by treatment with the detectably labeled antibody. The solid phase support can then be washed with the buffer a second time to remove unbound antibody. The amount of bound label on the solid support can then be detected by conventional means.

The most preferred methods for quantifying a biological marker for the purpose of carrying out the cancer prognosis method of the invention are described hereunder.

Quantifying biological markers by Tissue Microaray

In certain embodiments, a biological marker, or a set of biological markers, may be quantified with any one of the Tissue Microarray methods known in the art.

Tissue microarrays are produced by a method of re-locating tissue from conventional histologic paraffin blocks such that tissue from multiple patients or blocks can be seen on the same slide. This is done by using a needle to biopsy a standard histologic sections and placing the core into an array on a recipient paraffin block. This technique was originally described by Wan in 1987 (Wan, Fortuna and Furmanski ; Journal of Immunological Methods ). In certain embodiments of Tissue microarrays, tissue cores are placed in specific spatially fixed positions in a block. The technique is disclosed notably by Kononen et al. (Nature Medicine in 1998 ).

The tissue array technique involves acquiring small, minimal cylindrical samples from paraffin embedded tissue specimens. These cylinders are then arrayed in a systematic, high-density grid into another paraffin block.

For instance, tumor tissue samples, including under the form of biopsy samples (i) of the center of the tumor, (ii) of the invasion margin or (iii) of the regional lypmph nodes, are obtained from an appropriate number of individuals, formalin-fixed, paraffin-embedded tumor tissue blocks. These are transferred to a TMA block. Multiple TMA blocks can be generated at the same time. Each TMA block can be sectioned up to 300 times, with all resulting TMA slides having the same tissues in the same coordinate positions. The individual slides can be used for a variety of molecular analyses, such as H&E staining to ascertain tissue morphology, mRNA in situ hybridization, protein immunohistochemistry, or analysis of DNA for genetic alteration.

Because these cylindrical tumor tissue samples are small (0.4-1 mm diameter x 3-4 mm in height), up to a thousand tissues can be arrayed in a single paraffin block while minimizing the damage and tissue requirement. Furthermore, these paraffin blocks can be transversely cut into hundreds of tissue microarray sections, which can then each be used for different genetic analyses.

In addition to the increased speed of analyses, tissue microarrays may also ensure the reproducibility and reliability of the cancer prognosis method according to the invention, because the hundreds of different tissue samples are handled, prepared, and stained in a parallel, virtually identical manner all on the same slide ( Kallioniemi, O.; Wagner, U.; Kononen, J. and Sauter, G. Tissue microarray technology for high-throughput molecular profiling of cancer. Human Molecular Genetics (2001), 10, 657-662 .).

Typically, representative areas of the tumor are removed from paraffin-embedded tumor tissue blocks, whereby tumor tissue samples are obtained. Then, said tumor tissue samples are transferred to another recipient paraffin block where these samples are spotted. Then, the tissue sample spots that are arrayed in said recipient paraffin block are cut into thin sections, typically 2-5 µm sections, for further analysis.

Typically, for further analysis, one thin section of the array, namely the Tisue Microarray, is firstly incubated with labeled antibodies directed against one biological marker of interest. After washing, the labeled antibodies that are bound to said biological marker of interest are revealed by the appopriate technique, depending of the kind of label is borne by the labeled antibody, e.g. radioactive, fluorescent or enzyme label. Multiple labelling can also be performed simultaneously, especially in embodiments wherein more than one protein-specific antibody is used, for the purpose of quantifying more than one biological marker.

Illustrative embodiments of quantification of biological markers using Tissue Microarrays are disclosed in the examples herein.

Quantifying biological markers by immunohistochemistry on conventional tissue slides (paraffin-embedded or frozen specimens

In certain embodiments, a biological marker, or a set of biological markers, may be quantified with any one of the immunohistochemistry methods known in the art.

Analysis can then performed on (i) a global primary tumor (as a whole), (ii) a tissue sample from the center of the tumor, (iii) a tissue sample from the tissue directly surrounding the tumor which tissue may be more specifically named the "invasive margin" of the tumor and (iv) the lymph nodes located at the closest proximity of the tumor, (vi) a distant metastasis

Analysis can also, and preferably, be performed in combined tumor regions (defined above).

Typically, for further analysis, one thin section of the tumor, is firstly incubated with labeled antibodies directed against one biological marker of interest. After washing, the labeled antibodies that are bound to said biological marker of interest are revealed by the appopriate technique, depending of the kind of label is borne by the labeled antibody, e.g. radioactive, fluorescent or enzyme label. Multiple labelling can be performed simultaneously.

Quantifying biological markers by flow cytometry methods

In certain embodiments, a biological marker, or a set of biological markers, may be quantified with any one of the flow cytometry methods known in the art.

For example, cells contained in the tumor tissue sample being tested are firstly extracted by mechanical dipsersion and cell suspensions in liquid medium are prepared.

Then, the thus obtained cells are incubated during tha appropriate time period with antibodies specifically directed against the biological marker(s) that is (are) to be quantified.

After washing the cell suspension in order to remove the unbound antibodies, the resulting cells are analysed by performing flow cytometry, in view of quantifying the percentage of the total number of cells present in the cell suspension that express each of said biological marker(s).

Illustrative embodiments of quantification biological markers using flow cyometry methods are disclosed in the examples herein.

Quantifying biological markers by nucleic acid amplification

In certain embodiments, a biological marker, or a set of biological markers, may be quantified with any one of the nucleic acid amplification methods known in the art.

Analysis can then performed on (i) a global primary tumor (as a whole), (ii) a tissue sample from the center of the tumor (CT), (iii) a tissue sample from the tissue directly surrounding the tumor which tissue may be more specifically named the "invasive margin" of the tumor, (iv) the lymph nodes located at the closest proximity of the tumor, and (vi) a distant metastasis

Analysis can also, and preferably, be performed in combined tumor regions (defined above) after tumor microdissection.

The polymerase chain reaction (PCR) is a highly sensitive-and powerful method for such biological markers quantification

For performing any one of the nucleic acid amplification method that is appropriate for quantifying a biological marker when performing the cancer prognosis method of the invention, a pair of primers that specifically hybridise with the target mRNA or with the target cDNA is required.

A pair of primers that specifically hybridise with the target nucleic acid biological marker of interest may be designed by any one of the numerous methods known in the art.

In certain embodiments, for each of the biological markers of the invention, at least one pair of specific primers, as well as the corresponding detection nucleic acid probe, is already referenced and entirely described in the public "Quantitative PCR primer database", notably at the following Internet address : http://lpgws.nci.nih.gov/cgi-bin/PrimerViewer.

In other embodiments, a specific pair of primers may be designed using the method disclosed in the

US Patent n° US 6,892,141 to Nakae et al., the entire disclosure of which is herein incoporated by reference.

Many specific adaptations of the PCR technique are known in the art for both qualitative and quantitative detections. In particular, methods are known to utilize fluorescent dyes for detecting and quantifying amplified PCR products. In situ amplification and detection, also known as homogenous PCR, have also been previously described. See e.g. Higuchi et al., (Kinetics PCR Analysis: Real-time Monitoring of DNA Amplification Reactions, Bio/Technology, Vol 11, pp 1026-1030 (1993) ), Ishiguro et al., (Homogeneous quantitative Assay of Hepatitis C Virus RNA by Polymerase Chain Reaction in the Presence of a Fluorescent Intercalater, Anal. Biochemistry 229, pp 20-213 (1995) ), and Wittwer et al., (Continuous Fluorescence Monitoring of Rapid cycle DNA Amplification, Biotechniques, vol.22, pp 130-138 (1997 .))

A number of other methods have also been developed to quantify nucleic acids ( Southern, E. M., J. Mol. Biol., 98:503-517, 1975 ; Sharp, P. A., et al., Methods Enzymol. 65:750-768, 1980 ; Thomas, P. S., Proc. Nat. Acad. Sci., 77:5201-5205, 1980 ). More recently, PCR and RT-PCR methods have been developed which are capable of measuring the amount of a nucleic acid in a sample. One approach, for example, measures PCR product quantity in the log phase of the reaction before the formation of reaction products plateaus ( Kellogg, D. E., et al., Anal. Biochem. 189:202-208 (1990) ; and Pang, S., et al., Nature 343:85-89 (1990) ). A gene sequence contained in all samples at relatively constant quantity is typically utilized for sample amplification efficiency normalization. This approach, however, suffers from several drawbacks. The method requires that each sample have equal input amounts of the nucleic acid and that the amplification efficiency between samples be identical until the time of analysis. Furthermore, it is difficult using the conventional methods of PCR quantitation such as gel electrophoresis or plate capture hybridization to determine that all samples are in fact analyzed during the log phase of the reaction as required by the method.

Another method called quantitative competitive (QC)-PCR, as the name implies, relies on the inclusion of an internal control competitor in each reaction ( Becker-Andre, M., Meth. Mol. Cell Biol. 2:189-201 (1991) ; Piatak, M. J., et al., BioTechniques 14:70-81 (1993) ; and Piatak, M. J., et al., Science 259:1749-1754 (1993) ). The efficiency of each reaction is normalized to the internal competitor. A known amount of internal competitor is typically added to each sample. The unknown target PCR product is compared with the known competitor PCR product to obtain relative quantitation. A difficulty with this general approach lies in developing an internal control that amplifies with the same efficiency of the target molecule.

For instance, the nucleic acid amplification method that is used may consist of Real-Time quantitative PCR analysis.

Real-time or quantitative PCR (QPCR) allows quantification of starting amounts of DNA, cDNA, or RNA templates. QPCR is based on the detection of a fluorescent reporter molecule that increases as PCR product accumulates with each cycle of amplification. Fluorescent reporter molecules include dyes that bind double-stranded DNA (i.e. SYBR Green I) or sequence-specific probes (i.e. Molecular Beacons or TaqMan® Probes).

Preferred nucleic acid amplification methods are quantitative PCR amplification methods, including multiplex quantitative PCR method such as the technique dislosed in the published US patent Application n°

US 2005/0089862, to Therianos et al. , the entire disclosure of which is herein incorporated by reference.

Illustratively, for quantifying biological markers of the invention, tumor tissue samples are snap-frozen shortly after biopsy collection. Then, total RNA from a "tumor tissue sample" (i) a global primary tumor (as a whole), (ii) a tissue sample from the center of the tumor, (iii) a tissue sample from the tissue directly surrounding the tumor which tissue may be more specifically named the "invasive margin" of the tumor, (iv) the lymph nodes located at the closest proximity of the tumor, (v) a tumor biopsie perform prior surgery (for follow-up of patients after treatment for exemple), and (vi) a distant metastasis, is isolated and quantified. Then, each sample of the extracted and quantified RNA is reverse-transcribed and the resulting cDNA is amplified by PCR, using a pair of specific primers for each biological marker that is quantified. Control pair of primers are simultaneously used as controls, such as pair of primers that specifically hybridise with 18S cDNA and GADPH cDNA, or any other well known "housekeeping" gene.

Illustrative embodiments of quantification biological markers using nucleic acid amplification methods are disclosed in the examples herein.

Cancer prognosis kits

The invention includes a kit for assessing the prognosis of a cancer in a patient (e.g. in a sample such as a tumor tissue patient sample). The kit comprises a plurality of reagents, each of which is capable of binding specifically with a biological marker nucleic acid or protein. Suitable reagents for binding with a marker protein include antibodies, antibody derivatives, antibody fragments, and the like. Suitable reagents for binding with a marker nucleic acid (e.g. a genomic DNA, an mRNA, a spliced mRNA, a cDNA, or the like) include complementary nucleic acids. For example, the nucleic acid reagents may include oligonucleotides (labeled or non-labeled) fixed to a substrate, labeled oligonucleotides not bound with a substrate, pairs of PCR primers, molecular beacon probes, and the like.

Thus, a further object of this invention consists of a kit for the prognosis of progression of a cancer in a patient, which kit comprises means for quantifying at least one biological marker indicative of the status of the adaptive immune response of said patient against cancer.

The kit of the invention may optionally comprise additional components useful for performing the methods of the invention. By way of example, the kit may comprise fluids (e.g. SSC buffer) suitable for annealing complementary nucleic acids or for binding an antibody with a protein with which it specifically binds, one or more sample compartments, an instructional material which describes performance of the cancer prognosis method of the invention, and the like.

In certain embodiments, a kit according to the invention comprises one or a combination or a set of antibodies, each kind of antibodies being directed specifically against one biological marker of the invention.

In one embodiment, said kit comprises a combination or a set of antibodies comprising at least two kind of antibodies, each kind of antibodies being selected from the goup consisting of antibodies directed against one of the CD3, CD8, GZMB, CD45RO, GLNY, TBX21, IRF1, IFNG, CXCL9, CXCL10, CD4, CXCR3, CXCR6, IL-18, IL-18Rbeta, Fractalkine, IL-23, IL-31, IL-15, IL-7, MIG, Perforin, TCRαβ, TCRγδ, LAT, ZAP70, CD5, CD2biological markers.

An antibody kit according to the invention may comprise 2 to 20 kinds of antibodies, each kind of antibodies being directed specifically against one biological marker of the invention. For instance, an antibody kit according to the invention may comprise 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 or 20 kinds of antibodies, each kind of antibodies being directed specifically against one biological marker as defined herein.

Various antibodies directed against biological markers according to the invention, including CD3, CD8, GZMB, CD45RO, GLNY, TBX21, IRF1, IFNG, CXCL9, CXCL10, CD4, CXCR3, CXCR6, IL-18, IL-18Rbeta, Fractalkine, IL-23, IL-31, IL-15, IL-7, MIG, Perforin, TCRαβ, TCRγδ, LAT, ZAP70, CD5, CD2 are listed in Table 3.

In certain other embodiments, a kit according to the invention comprises one or a combination or a set of pair of ligands or specfic soluble molecules binding with one or more of the biological marker(s), of the invention.

In certain other embodiments, a kit according to the invention comprises one or a combination or a set of pair of primers, each kind of pair of primers hybridising specifically with one biological marker of the invention.

In one embodiment, said kit comprises a combination or a set of pair of primers comprising at least two kind of pair of primers, each kind of pair of primers being selected from the goup consisting of pair of primers hybridising with one of the CD3, CD8, GZMB, CD45RO, GLNY, TBX21, IRF1, IFNG, CXCL9, CXCL10, CD4, CXCR3, CXCR6, IL-18, IL-18Rbeta, Fractalkine, IL-23, IL-31, IL-15, IL-7, MIG, Perforin, TCRαβ, TCRγδ, LAT, ZAP70, CD5, CD2biological markers.

A primer kit according to the invention may comprise 2 to 20 kinds of pair or primers, each kind of pair of primers hybridising specifically with one biological marker of the invention. For instance, a primer kit according to the invention may comprise 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 or 20 kinds of pairs of primers, each kind of pair of primers hybridising specifically against one biological marker as defined herein.

Notably, at least one pair of specific primers, as well as the corresponding detection nucleic acid probe, that hybridise specifically with one biological marker of interest, is already referenced and entirely described in the public "Quantitative PCR primer database", notably at the following Internet address : http://lpgws.nci.nih.gov/cgi-bin/PrimerViewer.

Monitoring anti-cancer treatments

Monitoring the influence of agents (e.g., drug compounds) on the level of expression of a biological marker of the invention can be applied for monitoring the status of the adaptive immune ersponse of the patient with time. For example, the effectiveness of an agent to affect biological marker expression can be monitored during treatments of subjects receiving anti-cancer treatments.

In a preferred embodiment, the present invention provides a method for monitoring the effectiveness of treatment of a subject with an agent (e.g., an agonist, antagonist, peptidomimetic, protein, peptide, nucleic acid, small molecule, or other drug candidate) comprising the steps of (i) obtaining a pre-administration sample from a subject prior to administration of the agent; (ii) detecting the level of expression of one or more selected biological markers of the invention in the pre-administration sample; (iii) obtaining one or more post-administration samples from the subject; (iv) detecting the level of expression of the biological marker(s) in the post-administration samples; (v) comparing the level of expression of the biological marker(s) in the pre-administration sample with the level of expression of the marker(s) in the post-administration sample or samples; and (vi) altering the administration of the agent to the subject accordingly. For example, decreased expression of the biological marker gene(s) during the course of treatment may indicate ineffective dosage and the desirability of increasing the dosage. Conversely, increased expression of the biological marker gene(s) may indicate efficacious treatment and no need to change dosage.

As already mentioned previously in the present specification, performing the cancer prognosis method of the invention may indicate, with more precision than the prior art methods, those patients at high-risk of tumor recurrence who may benefit from adjuvant therapy, including immunotherapy.

For example, if, at the end of the cancer prognosis method of the invention, a good prognosis of no metastasis or a prognosis of a long time period of disease-free survival is determined, then the subsequent anti-cancer treatment will not comprise any adjuvant chemotherapy.

However, if, at the end of the cancer prognosis method of the invention, a bad prognosis with metastasis or a bad prognosis with a short time period of disease-free survival is determined, then the patient is administered with the appropriate composition of adjuvant chemotherapy.

Further, if, at the end of the cancer prognosis method of the invention, a bad prognosis with metastasis or a bad prognosis with a short time period of disease-free survival is determined, then the patient is administered with the appropriate immunostimulating composition, including pharmaceutical composition comprising an immunostimulating cytokine or chemokine, including interleukines.

Preferred immunostimulating cytokines or chemokines are selected from the group consisting of IL-1α, IL-1β, IL-2, IL-3, IL-4, IL-5, IL-7, G-CSF, IL-15, GM-CSF, IFN-γ, CXCL9, CXCL10, Fractalkine, MIG, IFNα, IL-18, IL-12, IL-23 and IL-31.

Accordingly, the present invention also relates to a method for adapting a cancer treatment in a cancer patient, wherein said method comprises the steps of :

1. a) performing, on at least one tumor tissue sample collected from said patient, the cancer prognosis method that is desclosed herein;
2. b) adapting the cancer treatment of said cancer patient by administering to said patient an adjuvant chemotherapy or an immunostimulating therapy if a bad cancer prognosis is determined at the end of step a).

Another object of the invention consists of a kit for monitoring the effectiveness of treatment (adjuvant or neo-adjuvant) of a subject with an agent, which kit comprises means for quantifying at least one biological marker indicative of the status of the adaptive immune response of said patient against cancer.

The present invention is further illustrated by, without in any way being limited to, the examples below.

EXAMPLES

A. MATERIAL AND METHODS OF THE EXAMPLES

A.1. Patients and database

All cases of colorectal cancer (n=959) who underwent a primary resection of the tumor at the Laennec/HEGP Hospital between 1986 and 2004 were reviewed. The observation time in this unselected cohort was the interval between diagnosis and last contact (death or last follow-up). Data were censored at the last follow-up for patients who had not relapsed or who had died. The mean duration of follow-up was 44.5 months. Six patients lost to follow-up were excluded from the analysis. Histopathological and clinical findings were scored according to the UICC-TNM staging system ( Sobin LH, Wittekind C, (eds). UICC TNM classification of malignant tumors. 6th ed. New York: Wiley-Liss, 2002 ), as disclosed in Table 1. Early metastatic invasion was defined as the presence of vascular emboli (VE), lymphatic invasion (LI), or perineural invasion (PI). A VELIPI-positive tumor had at least one of these pathologic findings, whereas a VELIPI-negative tumor had none of the three findings. TNM and VELIPI status of the tumors were determined from the histopathological reports at the time of resection. A secure Web-based database, TME.db (Tumoral MicroEnvironment Database), was built on a 3-tier architecture using Java-2 Enterprise-Edition (J2EE) to integrate clinical data sets, and the results from high-throughput technologies.

The records of 959 Patients with colorectal who underwent a primary resection at the Laennec/HEGP Hospital between 1986 and 2004 were retrospectively reviewed representing a prospective, continuous, unselected cohort of patients. Conventional histopathological parameters, including AJCC/UICC TNM stage, Duke's stage, tumor type, and grade of differentiation, lymphovascular embolies, perineural tumor invasion (VELIPI), are detailed in Table 1. Data on adjuvant and palliative chemotherapy were recorded. Adjuvant with 5-fluorouracil (FU)-based chemotherapy was administered to 327 patients (57 patients with stage II disease, 136 with stage III disease, and 134 with stage IV disease).

Postsurgery patient surveillance was carried-out, at Laennec/HEGP and associated hospitals, for all patients according to general practice for colon cancer patients, including physical examination, blood counts, liver function tests, serum carcinoembryonic antigen, abdominal ultrasonography and computed tomography scan, and pulmonary x-ray. Colonoscopy was performed one year after resection and then once every three years if normal. If tumor relapse was suspected, the patient underwent intensive work-up, including abdominal computed tomography scan, magnetic resonance imaging, chest x-ray, colonoscopy, and biopsy, when applicable.

Clinical findings, treatment, histopathologic report, and follow-up data were collected prospectively and updated (by A.B) and included into TME.db. The database is accessible upon request to zlatko.trajanoski@tugraz.at. Observation time was the interval between diagnosis and last contact (death or last follow-up). Data were censored at the last follow-up for patients without relapse, or death. The mean duration of follow-up was 44.5 months. The min:max values until progression/death or last follow-up were (0:214) months, respectively. Six patients lost to follow-up were excluded from the analysis. Time to recurrence or disease-free time was defined as the time period from the date of surgery to confirmed tumor relapse date for relapsed patients and from the date of surgery to the date of last follow-up for disease-free patients.

A.2. Histopathology

All the H&E sections of the tumors for each patient were reassessed blindly by two pathologist (D.D., T.M.) or two investigators (F.P., J.G.) trained in the pathology of colonic cancer, for each of the following: (a) tumoral lymphoid infiltrate (b) lymphoid reaction at the invasive margin (10 to 20 fields analyzed per-patient). The densities of these immune infiltrates were scored independently by the investigators, as weak (score 1), moderate (score 2), or strong (score 3), as disclosed hereunder.

Three hundred seventy seven randomly selected tumors from the 415 tumors evaluated by TMA were reassessed for immune cell density. Review of tissue sections was performed independently by two pathologists (D.D., T.M.) or two investigators (F.P. and J.G.) trained in the pathology of colonic cancer. A mean number of four sections of primary tumor were analyzed. The fields analyzed were chosen as representative of the region, and were at distance from necrotic material or abscesses. Immune infiltrates were scored as followed:

(a) tumoral lymphoid infiltrates:

The density of tumoral lymphoid infiltrates was quantified by counting the small round lymphocytes distributed within the tumor epithelium and the peritumoral stroma in five medium power fields (Nikon microscope, x 20 objective). Immune-infiltrate density scored as 1 (weak), 2 (moderate) or 3 (strong), was observed in 16%, 62% and 22% of the series, respectively.

(b) lymphoid reaction at the invasive margin:

The cuff of lymphocytes abutting the deepest point of advancing tumor (invasive margin) was judged as, conspicuous (score 3), inconspicuous (score 2) or absent (score 1). The lymphoid reaction scored as 1, 2 or 3, was observed in 18%, 60% and 22% of the series, respectively.

(c) lymphoid nodules surrounding the periphery of the tumor:

Crohn's-like lymphoid reaction was defined as lymphoid aggregates (often with germinal centers) ringing the periphery of invasive carcinoma typically found at the interface of the muscularis propria externa and pericolic fibro-adipose tissue, not associated with either mucosa (eg, diverticular origin) or pre-existing lymph node. Two large lymphoid aggregates in a section were required for the presence of this feature (score 2). More than two large lymphoid aggregates referred to score 3, whereas only one or an absence of lymphoid aggregates was scored 1. The density of lymphoid nodules scored as 1, 2 or 3, was observed in 38%, 39% and 23% of the series, respectively.

Real-Time Taqman PCR analysis

Total RNA was extracted from 100 randomly selected frozen tumor specimens from the cohort of 959 cases; 75 samples of sufficient quality and quantity were analyzed for gene expression using quantitative real-time TaqMan-PCR (Low-Density-Arrays) and the 7900 robotic PCR-system (Applied-Biosystems, Foster City, CA), as disclosed hereunder.

Tissue samples were snap-frozen within 15 minutes following surgery and stored in liquid N ₂ . Randomly selected frozen tumor specimens (n=100) from the cohort were extracted for RNA. Total RNA was isolated by homogenization with RNeasy isolation-kit (Qiagen, Valencia, CA). The integrity and the quantity of the RNA were evaluated on a bioanalyzer-2100 (Agilent Technologies, Palo Alto, CA). Seventy-five samples were of sufficient RNA quality and quantity for Low-Density-Array analysis. These samples, representative of the cohort, were all assessed for gene expression analysis. RT-PCR experiments were carried-out according to the manufacturer's instructions (Applied-Biosystems, Foster City, CA). Quantitative real-time TaqMan-PCR was performed using Low-Density-Arrays and the 7900 robotic real-time PCR-system (Applied-Biosystems) (see list of genes in Table 2 for details). 18S and GAPDH primers and probes were used as internal controls. Data were analyzed using the SDS Software v2.2 (Applied-Biosystems).

Tissue samples were snap-frozen within 15 minutes following surgery and stored in liquid N ₂ . Randomly selected frozen tumor specimens (n=100) from the cohort (n=959), were extracted for RNA. Total RNA was isolated by homogenization with RNeasy isolation-kit (Qiagen, Valencia, CA). The integrity and the quantity of the RNA were evaluated on a bioanalyzer-2100 (Agilent Technologies, Palo Alto, CA). Seventy-five samples were of sufficient RNA quality and quantity for Low-Density-Array analysis. These samples, representative of the cohort, were all assessed for gene expression analysis. RT-PCR experiments were carried-out according to the manufacturer's instructions (Applied-Biosystems, Foster City, CA). Quantitative real-time TaqMan-PCR was performed using Low-Density-Arrays and the 7900 robotic real-time PCR-system (Applied-Biosystems) (see list of genes in Table 2 for details). 18S and GAPDH primers and probes were used as internal controls. Data were analyzed using the SDS Software v2.2 (Applied-Biosystems).

Large-scale flow-cytometric analysis

Cells were extracted by mechanical dispersion from 39 fresh tumor samples. All cells (including tumor cells) were analyzed by flow-cytometry. Cells from normal mucosa from a site that was distant from the fresh tumor were also analyzed. Cells were incubated for 30 minutes at 4°C with antibodies against immune cell markers (See Table 3 for the list of antibodies). Analyses were performed with a four-color-FACScalibur flow cytometer and CellQuest software (Becton Dickinson, San Diego, CA). Immune subpopulations were measured as a percentage of the total number of all cells and a percentage of the number of total CD3+ cells. Average-linkage hierarchical clustering was applied and the results were displayed using the GENESIS program ( Sturn A, Quackenbush J, Trajanoski Z. Genesis: cluster analysis of microarray data. Bioinformatics 2002; 18(1):207-8 ; Galon J, Franchimont D, Hiroi N, et al. Gene profiling reveals unknown enhancing and suppressive actions of glucocorticoids on immune cells. Faseb J 2002;16(1):61-71 ; software available at http://www.genome.tugraz.at).

Tissue Microarray Construction

Using a tissue-array instrument (Beecher Instruments, ALPHELYS, Plaisir, France), two representative areas of the tumor (center and invasive margin) were removed (0.6mm and 1mm-diameter punches, respectively) from paraffin-embedded tissue-blocks that were prepared at the time of resection. Of the colonic carcinomas that were resected between 1990 and 2003, 50 percent (415) were randomly selected for construction of tissue microarrays. Based on T, N, M, VELIPI pathologic findings, patients with these tumors were representative of the entire cohort. Tissue-cores arrayed into recipient paraffin-blocks were cut into 5µm sections for Harris's hematoxylin (HE) and immunohistochemical staining.

Immunohistochemistry

Following antigen retrieval and quenching of endogen-peroxidase activity, sections were incubated (60 min. at room temperature) with monoclonal antibodies against CD45RO and CD3 (Neomarkers, Fremont, CA). Envision+ system (enzyme-conjugated polymer backbone coupled to secondary antibodies) and DAB-chromogen were applied (Dako, Copenhagen, Denmark). Tissue sections were counterstained with Harris' hematoxylin. Isotype-matched mouse monoclonal antibodies were used as negative controls. Slides were analyzed using an image analysis workstation (Spot Browser®, ALPHELYS). Polychromatic high-resolution spot-images (740x540 pixel, 1.181 µm/pixel resolution) were obtained (x100 fold magnification). Measurements were recorded as the number of positive cells per-tissue surface-unit.

Statistical analysis

Kaplan-Meier curves were used to assess the influence of pathologic signs of early metastatic invasion (VELIPI) on overall and disease-free survival. The significance of various clinical parameters was assessed by univariate analysis using the log-rank test (Table 1). We used a Cox proportional-hazards model to test the simultaneous influence on survival (overall and disease-free) of all covariates found significant in the univariate analysis. The same tests were used to assess the effect of the density of CD45RO (number of cells/mm ² ) on overall and disease-free survival, alone or together with the tumor, node, and metastases (T, N, M) staging covariates. The Anova-t test and the Wilcoxon-Mann-Whitney test were the parametric and non-parametric tests used to identify markers with a significantly different expression among VELIPI-positive and VELIPI-negative tumors. Normality of the logarithm of the gene expression levels and of the CD45RO densities was determined using the Shapiro test. The Wilcoxon test was used to assess the significance of the difference between median survivals across different groups of patients. All tests were two-sided. A P-value < 0.05 was considered statistically significant. All P-values are reported without multiple correction adjustments. All analyses were performed with the statistical software R and Statview.

B. RESULTS

Example 1: Correlation between clinical outcome and adptive immune response

1.1 Early metastatic invasion and clinical outcome

The prognostic significance of the presence of vascular emboli (VE), lymphatic invasion (LI), and perineural invasion (PI), which delineated early metastatic invasion (VELIPI), was investigated by univariate analysis of the 959 colorectal cancer patients. VE, LI, PI, and VELIPI as well as the T, N, M stage significantly influenced disease-free and overall survival ( P <0.001) (Table 1).

The five-year disease-free survival rates were 32.4 percent among patients with VELIPI-negative tumors, and 12.1 percent among patients with VELIPI-positive tumors, respectively. Differences were also observed in the median duration of disease-free survival (3.3 vs 26.9 months for VELIPI-positive and VELIPI-negative tumors, respectively, P<0.001). A similar pattern was found for overall survival (Table 1).

Furthermore, the presence of more than one sign of early metastatic invasion conferred a worse prognosis that a single sign. Kaplan-Meier curves suggested longer overall survival and disease-free survival in patients with VELIPI-negative tumors than in patients with VELIPI-positive tumors (log-rank test, P<0.001). VE, LI, or PI correlated with the N and M stages (P<0.001 for all comparisons). The influence of all significant covariates on survival was simultaneously tested using a Cox proportional-hazards model. Multivariate analysis adjusting for TNM staging confirmed that VELIPI status was significantly and independently associated with a better prognosis (P=0.04 and P=0.01 for overall and disease free survival, respectively). Adjusting for Dukes staging, VELIPI status was independently associated with a better prognosis (P=0.007 and P=0.002 for overall and disease free survival, respectively).

1.2 Immune cell infiltration, inflammation, early-metastatic invasion, and prognosis

Colorectal tumors (n=377) were assessed histopathologically for an immune cell infiltrate within the tumor and in the invasive margin.

The presence of strong immune infiltrate (score 3) was associated with VELIPI-negative tumors.

The mRNAs for proinflammatory and immunosuppressive molecules in 75 colorectal tumors were measured by low density array quantitative real-time PCR. No significant association between content of mRNA for proinflammatory mediators (IL-8, VEGF, CEACAM-1, MMP-7, Cox-2 and thrombospondin-1), or for immunosuppressive molecules (TGFβ, IL-10, B7-H3, and CD32b) and VELIPI status or relapse was found (Figure 1 and data not shown).

T-cells differentiate into T _H 1 or T _H 2 cells following expression of T-bet or GATA-3, respectively. Protective immune responses are mediated by effector-memory T-cells with the phenotype CD8+, CD45RO+, CCR7-, CD62L-, perforin+, granulysin+, granzyme-B+. These cells exert an immediate effector function upon antigen stimulation by releasing cytotoxic mediators. As shown in Figure 1, CD8α, granulysin, and granzyme-B were increased in VELIPI-negative tumors and were further increased in such tumors from patients who had not relapsed, as compared with VELIPI-positive tumors from patients who had relapsed (P<0.05).

Moreover, VELIPI-negative tumors from patients who had not relapsed had a significant increase in the T _H 1 mediators T-bet, IRF-1, and IFN-γ compared to VELIPI-positive tumors from patients who had relapsed ( P <0.05). In contrast, the T _H 2 transcription factor, GATA-3, was not increased in either group of patients (Figure 1).

Example 2 : Phenotypes of tumor-infiltrating immune cells

Subpopulations of immune cells from 39 freshly resected colon cancers were analyzed by large-scale flow-cytometry. To refine the analysis, 410 different combinations of surface markers were measured by FACS, and the results were plotted from the minimum to the maximum expression.

T-cells, B-cells, NK-cells, NKT-cells, and macrophages were analyzed in relation to the VELIPI status of the tumors. CD3+ T-cells were the most prevalent tumor-infiltrating immune cells. CD3+, CD3+CD4+, and CD3+CD8+ T-cells were significantly increased (2.6, 2.5, 4.9 fold increase, respectively, P<0.05) in VELIPI-negative tumors compared to VELIPI-positive tumors.

Large-scale analysis of phenotypic and functional markers of T-cell subpopulations (percentage of positive cells in the total population isolated from the tumor and within the CD3+T-cell population) revealed a significant difference ( P <0.05) between VELIPI-negative and VELIPI-positive tumors for 65 different combinations of markers. Hierarchical clustering ( Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A 1998;95(25):14863-8 ) showed a homogeneous pattern in VELIPI-positive tumors, whereas two subgroups of VELIPI-negative tumors could be distinguished.

All markers (CD45RO, CD45RA, CD27, CD28, CCR7, CD127) of the T-cell differentiation process from naïve to effector-memory T-cells were present in the cluster of differentially expressed markers. Markers of T cell migration (CD62L-, CCR7-, CD103, CD49d, CXCR3) and activation (HLA-DR, CD98, CD80, CD86, CD134) were also differentially expressed between VELIPI-negative and VELIPI-positive tumors.

The results have shown that naïve T cells (CD3+CCR7+) were rare in the tumors. By contrast, in the differentiation pathway from early-memory T cells (CD45RO+CCR7-CD28+CD27+) to effector-memory T-cells (CD45RO+CCR7-CD28-CD27-), all subpopulations were detected. Compared with VELIPI-positive tumors, VELPI-negative tumors had significantly more of these T cells (P<0.05). The results show that the high proportion of mature CD8+ T-cells in VELIPI-negative tumors. In contrast to tumors, distant normal mucosa from the same patients did not exhibit differences in the CD8+ T-cell subpopulations according to the VELIPI status.

Example 3 : Effector-memory T cells and survival

Immunohistochemical analysis on Tissue-MicroArrays prepared from 415 colorectal cancers was performed. Staining with an anti-CD3 antibody revealed the presence of T-cells both within and at the invasive margin of the tumor. CD45RO+ cells were counted by automatic image software. A validation study showed a close correlation between optical and automatic cell counts (R ² =0.914, P <0.001).

VELIPI-negative tumors contained high numbers of CD45RO cells as compared to VELIPI-positive tumors (P=0.02). In addition, high density of memory T-cells was associated with lymph-node negative (N-) and metastasis negative (M-) tumors ( P <0.001).

Advanced stages of lymph-node invasion (N2, N3) were associated with low densities of CD45RO in tumors (Figure 2).

Multivariate Cox proportional-hazard analysis showed that M ( P <0.001), N (P=0.002), and T (P=0.004) as well as CD45RO (P=0.02) were independent prognostic factors for overall survival. Kaplan-Meier curves suggested longer overall survival and disease-free survival (Figure 3A, 3B) in patients with tumors containing high density of CD45RO than in patients with low density (log-rank test, P<0.001). Patients whose tumors contained high density of CD45RO had a median disease-free survival of 36.5 months and a median overall survival of 53.2 months, as compared with 11.1 and 20.6 months, respectively, among patients with low density of CD45RO (P<0.001 for all comparisons) (Figure 3A, 3B). The five-year overall (Figure 3A) and disease-free survival (Figure 3B) rates were 46.3 and 43.1 percent among patients with tumors containing high density of CD45RO and 23.7 and 21.5 percent among patients with tumors containing low density of CD45RO.

Results of Examples 1 to 3 above show that there exists a relation between pathologic signs of early metastatic invasion (vascular emboli (VE), lymphatic invasion (LI), and perineural invasion (PI), collectively termed VELIPI) and the outcome in 959 colorectal cancers.

It has also been shown the existence of an association between the VELIPI status of the tumor and evidence of an immune response within the tumor. In particular, an analysis of 39 colorectal cancers showed that the presence of intratumoral effector-memory T-cells, defined by CD3, CD8, CD45RO, CCR7, CD28, and CD27 markers, was associated with VELIPI-negative tumors. Analysis of 415 colorectal tumors showed that high density of infiltrating CD45RO+ cells correlated with a good clinical outcome.

In the series of 959 colorectal cancers herein, emboli detected by meticulous pathological examination showed a significant, independent association between VELIPI status and overall survival.

In examples 1 to 3 above, no significant differences were found in the content of mRNAs for proinflammatory and immunosuppressive molecules in VELIPI-positive and VELIPI-negative tumors, or in tumors from patients who did or did not relapse. These findings suggest that inflammation is not a factor in early metastatic invasion.

In contrast, there was increased mRNA for products and markers of T _H 1 effector T-cells (CD8, T-bet, IRF-1, IFN-γ, granulysin, and granzyme-B), and this increase was associated with prolonged survival and a lack of pathological signs of early metastatic invasion.

Using tissue microarrays, the association between a high number of CD45RO+ T-cells and the absence of lymphovascular and perineural invasion ( P <0.002) was shown in Examples 1 to 3 above. Tumors that contained high density of effector-memory T cells were associated with longer disease-free and overall survival than tumors lacking such cells ( P <0.001). The presence of CD45RO-positive memory T-cells in the tumor was an independent prognostic factor.

In examples 1 to 3, the high-throughput quantitative measurement of cellular and molecular differences among colorectal cancers allowed a detailed characterization of the tumor micro-environment, and identification of associations with clinical outcome. The experimental results show that the tumor microenvironment and the host's immune response are of major importance in tumor progression.

Thus, it has been shown in Examples 1 to 3 above that univariate analysis showed significant differences in disease-free and overall survival according to the presence or absence of histological signs of early metastatic invasion ( P <0.001). By multivariate Cox analysis, pathologic stage (T, N, M) ( P <0.001) and early metastatic invasion (P=0.04) were independently associated with survival. Tumors lacking signs of early metastatic invasion had infiltrates of immune cells and increased mRNA for products of T _H 1 effector T-cells (CD8, T-bet, IRF-1, IFN-□, granulysin, and granzyme-B).

In contrast, neither proinflammatory mediators nor immunosuppressive molecules were differentially expressed. In tumors with or without early signs of metastatic invasion there were significant differences for 65 combinations of T-cell markers, and hierarchical clustering showed that markers of T cell migration, activation, and differentiation were increased in tumors lacking these signs.

These tumors contained increased numbers of CD8-positive T cells, ranging from early-memory (CD45RO+CCR7-CD28+CD27+) to effector-memory (CD45R0+CCR7-CD28-CD27-) T-cells. The presence of infiltrating memory CD45RO+ cells, evaluated by immunohistochemistry, correlated with signs of early metastatic invasion, pathological stage, and survival.

It has thus been shown that signs of an immune response within colorectal cancers are associated with the absence of pathological evidence of early metastatic invasion and prolonged survival.

Example 4 : Correlation between (i) adaptive immune response and (i) recurrence and survival times

Functional orientation of the host-response within colorectal cancers was investigated by quantitative real-time PCR through the evaluation of 18 immune-related genes. These genes were variably expressed among the 75 tumours studied.

Correlation analyses performed between all genes (representing 153 correlation tests) showed 70 significant combinations ( P <0.05) including 39 highly significant combinations ( P <0.0001) (See Table 4).

A correlation-matrix was generated, followed by unsupervised hierarchical clustering offering a convenient way to visualize patterns of similarity and difference among all correlations. This allowed the identification of a dominant cluster of co-modulated genes, composed of T _H1 (T-bet, IRF-1, IFN _γ ) and immune-adaptive (CD3ζ, CD8, GLNY, GZMB) related genes, and two clusters referring to proinflammatory and immune-suppressive mediators.

Expression patterns of the clusters were almost mutually exclusive in the tumours. Expression levels of genes from the T _H1 /adaptive cluster inversely correlated with relapse, whereas the others (VEGF, MMP-7, Cox-2, IL-8, Survivin, CEACAM1, TRAIL-R, B7H3, IL-10, TGFb) did not.

A hierarchical tree structure classifying the 75 colorectal cancers according to the mRNA levels of genes from the T _H1 /adaptive cluster (from maximal to minimal expression levels) showed progressive recurrence rates from 20% to 80%, (Fisher exact test comparing group 1 and group 2, P=0.016). Patients with a homogeneous increased pattern of T _H1 /adaptive gene expression in tumour were associated with the best prognosis.

Altogether, these data provided evidence for a beneficial effect of in situ T _H1 /adaptive immunity on clinical outcome.

Then, the cellular end results of the immune-adaptive gene expression profiles was assessed by immunohistochemical-based Tissue-MicroArrays analysis of 415 tumours.

Furthermore, distribution of the in situ adaptive immune response was explored by spotting the centre of the tumour (CT) along with the invasive margin (IM).

In both tumour regions, immunostaining for total T lymphocytes (CD3), CD8 T cell effectors and associated cytotoxic molecule (GZMB), and memory T cells (CD45RO) showed a wide spectrum of positive-immune cell densities among all sample tested.

The 6640 corresponding immunostainings were analysed with a dedicated image analysis workstation for signal quantification (captured spot), allowing precise cell density measurements.

A validation study showed a close correlation between optical and automatic cell counts (R ² >0.9, P<0.001 for all markers).

Immune cell distributions in specific regions were analyzed in relation to the clinical outcome. Tumours from patients without relapse had a significantly higher immune cell density (CD3, CD8, CD45RO, GZMB) within each tumour region (CT or IM) (all P<0.003), than tumours from patients with relapse (Fig. 4).

Based on computer-signal quantification, a mean was devised to test the cut-off values of stained cells densities (for all markers in both tumour regions) for discrimination of patients for disease-free and overall survival times (1600 Log-rank tests). This permitted to define the optimal cut-off values of immune cell densities (CD3, CD8, CD45RO, GZMB), and to show that there was a large range of cut-off values in the two tumour regions that were significant (Figure 5). According to these cut-off values, it was observed that immune-cell infiltrates (high or low densities for CD3, CD8, CD45RO, GZMB) in each region of the tumour (CT or IM) markedly distinguished patients (n=415) into groups with different median disease-free survival (DFS) (Fig. 5). Log-rank tests were highly significant for all markers studied in both tumour regions for DFS (P-values ranging from 1.5x10 ^-4 to 1.4x10 ^-8 ) (Fig. 5 and Table 5) and for OS (Table 6).

It was further investigated whether the architectural distribution of the immune cells populations within the tumour (CT/IM) could influence prognosis. Kaplan-Meier curves for DFS and OS were analysed for patients with high or low CD3 densities in both tumour regions. This showed that high CD3 _CT /CD3 _IM densities resulted in significantly better overall and disease-free survival as compared to high CD3 density in a single region ( P <0.0001) (Fig. 7A, 7B). The combined analysis of CT plus IM regions further increased median DFS differences between patients with High and Low densities for all adaptive immune markers (P-values ranging from 3.7x10 ^-7 to 5.2x10 ^-11 ), as compared to single analysis of CT or IM regions (Fig. 6 and Fig. 7C). Thus, median DFS for low- and high-patients were of 5.9 vs 45.9 months for CD3 _CT , 12.9 vs 47.8 months for CD3 _IM (Fig. 6), and of 5.9 vs 66.2 months for CD3 _CT /CD3 _IM , respectively (Fig. 7C and Table 4). Taken together, these observations indicate that disease-free and overall survival times can be predicted on the basis of the architectural distribution and of the amplitude of the in situ coordinated adaptive immune response in distinct tumour regions.

Colorectal cancer prognosis is currently based on histopathologic criteria of tumour invasion. Cox proportional-hazards regression-models adjusted for TNM-stages and tumour differentiation showed that CD3 _CT /CD3 _IM density was an independent prognosticator for disease-free and overall survival ( P =2.8x10 ^-6 , P =3.0x10 ^-3 , respectively) (Table 5). Remarkably, CD3 _CT /CD3 _IM densities was the most significant parameter associated with disease-free survival and had a better P-value than those of T and N stages for overall survival analysis. Furthermore, all adaptive immune markers also presented with an independent prognostic value adjusted for TNM-stages and tumour differentiation for disease-free and overall survival (Table 7).

Conventional staging of colorectal cancer does not account for the marked variability in outcome that exits within each stage. As the nature and the amplitude of the in situ immune response hold a strong independent prognostic value, we investigated whether evaluation of this coordinated immune response could further predict patient outcomes at each stage. We stratified patients according to Dukes' classification (Fig. 8a) and showed an influence of CD3 _cT /CD3 _IM at all stages of the disease (Fig. 8b).

Unexpectedly, we found that a strong in situ coordinated adaptive immune response correlated with an equally favourable prognosis regardless of the tumour invasion through the intestinal wall and extension to the local lymph-nodes (Dukes classification A, B, C). Conversely, a weak in situ adaptive immune response correlated with a very poor prognosis even in patients with minimal tumour invasion (Dukes classification A and B) (Fig. 8b).

In examples 1 to 3 above, it was demonstrated that the absence of early signs of tumour dissemination (lymphovascular and perineural invasion) and of invasion of lymph-nodes was associated with the presence of a strong density of intratumoral effector-memory T-cells (T _EM ).

It is in the present example further determined whether additional evaluation of memory (CD45RO+) cell density to the CD3+T cell density in both tumour regions further discriminate patients at risk of tumour recurrence. CD3 _CT /CD3 _IM /CD45RO _CT /CD45RO _IM markedly stratified patients in two groups with high and low risk of tumour recurrence (Fig. 8c). Strikingly, low densities of these markers in both tumour regions revealed similar outcome for patients of Dukes C, B, and even A stages as compared to patients with concomitant distant metastasis (Dukes D).

Here, using high-throughput quantitative measurement of cellular and molecular immune parameters, the immunological forces in the microenvironment of human colorectal carcinoma was characterised.

Whatever the role of immunosurveillance and shaping, the present data clearly demonstrate the concept that once human colorectal carcinomas become clinically detectable, in situ natural anti-tumour immunity plays a major role in the control of tumour recurrence following surgical excision.

Beneficial in situ adaptive immune responses were not restricted to patients with minimal tumour invasion, indicating that in situ immunological forces might persist along with tumour progression. The possibility cannot be excluded that intra-tumoral lymphocytes modify tumour stroma or tumour cells, or both, in such a way that they attenuate the metastatic capacity of tumour cells.

However, the correlation of the expression of in situ adaptive immune markers, T _H1 -associated molecules, and cytotoxic mediators with a low incidence of tumour recurrence provides evidence for immune-mediated rejection of persistent tumour cells following surgery.

In this way, the good prognosis value associated with the presence in situ of high density of memory-T cells (CD45RO positive cells), probably results from the critical trafficking properties and the long-lasting anti-tumour protection capacity of these cells, as shown in mice model ¹⁸
The present evaluation of the in situ adaptive immune response that is performed, using quantitative measurements of immune cell densities at both the centre and the margin of the tumour, uncovered the importance of a coordinated adaptive immune response to control tumour recurrence.

Unexpectedly, the immunologic criteria, that are herein used, not only had a prognostic value that was superior and independent of those of the TNM and Dukes classifications, but also correlated with an equally favourable prognosis regardless of the tumour invasion.

Time to recurrence and overall survival time is shown to be governed more by the state of the local adaptive immune response rather than the presence of tumour spreading through the intestinal wall and to regional lymph node(s).

This novel insight has several important implications and may change the understanding of the evolution of carcinoma, including colorectal carcinoma. In addition, the criteria that have been used, should lead to a re-evaluation of the currently used classification of colorectal carcinoma, and indicate with more precision, those patients at high-risk of tumour recurrence who may benefit from adjuvant therapy (including immunotherapy).

The utility of immunohistochemistry, combined with the availability of an extensive set of antibodies against immune markers, should facilitate the application of our approach to other tumours.

Table 1: Disease-free and Overall Survival (n=959 patients)
	Disease Free Survival (DFS)					Overall Survival (OS)
	N. of patients	Rate at 5 yr %	Median months	P value*		Rate at 5 yr %	Median months	P value*
T stage				<0.001	s			<0.001 s
pTis	39	48.7	55.7			48.7	55.7
pT1	54	42.6	52.2			44.4	53.8
pT2	156	40.4	43.6			44.2	49.1
pT3	502	23.7	16.5			26.7	25.8
pT4	208	16.8	1.6			17.8	16.8
N stage				<0.001	s			<0.001 s
N0	568	35.4	34.6			38.6	43.1
N+	384	15.1	4.3			16.7	16.9
M stage				<0.001	s			<0.001 s
M0 747		34.5	32.6			37.6	41.1
M+	212	0.5	6.1			0.9	12.3
Dukes classification				<0.001	s			<0.001 s
A	83	47.0	55.6			47.0	55.6
B	438	37.2	39.2			41.1	46.8
C	227	24.7	19.5			27.3	28.1
D	212	0.5	0.1			1.0	12.1
Sex				0.38	ns			0.47 ns
Male	494	25.9	16.4			28.5	29.4
Female	465	28.2	19.3			30.5	27.3
Localization				0.20	ns			0.14 ns
RC	243	23.9	14.5			24.7	19.7
TC	51	7.8	9.2			9.8	22.2
LC	84	28.6	15.3			31.0	27.2
SC	298	26.8	14.7			29.5	29.5
R	287	32.4	32.1			36.5	40.4
Differentiation				0.26	ns			0.09 ns
Well	737	30.7	21.7			33.6	33.2
Moderate	187	14.4	9.3			15.5	17.8
Poor	35	17.1	2.6			17.1	11.6
Mucinous Colloid				0.087	ns			0.270 ns
No	766	28.2	19.5			30.9	30.9
Yes	193	22.3	14.9			23.8	21.8
N. of lymph nodes	analyzed			0.11	ns			0.69 ns
<8	426	34.0	31.0			37.1	40.0
≥8	533	21.4	12.9			23.5	23.2
VE				<0.001	s			<0.001 s
No	797	31.0	23:6			33.9	34.1
Yes	162	7.4	1.4			8.0	13.9
LI				<0.001	s			<0.001 s
No	803	29.5	21.6			32.1	32.0
Yes	156	14.1	0.5.			16.0	16.1
PI				<0.001	s			<0.001 s
No	860	29.3	20.7			32.0	32.0
Yes	99	7.1	0.1			8.1	16.2
VELIPI (VE or LI or	PI)			<0.001	s			<0.001 s
No	702	32.4	26.9			35.5	35.5
Yes	257	12.1	3.3			13.2	16.8
VE or LI				<0.001	s			<0.001 s
No	716	31,6	24,4			35,2	35,0
Yes	243	13,6	3,7			12,8	16,3
VE and LI				<0.001	s			<0.001 s
No	884	28,3	19.7			31,2	31,0
Yes	75	12.0	0,2			9,3	11,9
VE and LI and PI				<0.001	s			<0.001 s
No	911	28,0	19,5			30,7	30,5
Yes	48	8,3	0.1			6,3	9,5
RC: Right Colon, TC: Transverse Colon, LC: Left Colon, SC: Sigmoid Colon, R: Rectum VE: Vascular Emboli, LI: Lymphatic Invasion, PI: Perineural Invasion * P value LogRank test

Table 2 : List of genes
gene	name	Acc. number	Chr. Loc.
IL10	interleukin 10	NM_000572	1q31-q32
IL8	interleukin 8	NM_000584	4q13-q21
IFNG	interferon, gamma	NM_000619	12q14
TGFB1	transforming growth factor, beta 1	NM_000660	19q13.2
PTGS2	prostaglandin-endoperoxide synthase 2 (Cox2)	NM_000963	1q25.2
CEACAM1	carcinoembryonic antigen-related cell adhesion molecule 1	NM_001712	19q13.2
IRF1	interferon regulatory factor 1	NM_002198	5q31.1
MMP7	matrix metalloproteinase 7 (matrilysin, uterine)	NM_002423	11q21-q22
VEGF	vascular endothelial growth factor	NM_003376	6p12
GZMB	granzyme B	NM_004131	14q11.2
TBX21	T-box 21 (T-bet)	NM_013351	17q21.2
B7H3	B7 homolog 3	NM_025240	15q23-q24
CD8A	CD8 antigen, alpha polypeptide (p32)	NM_001768	2p12
GNLY	granulysin	NM_006433	2p12-q11
BIRC5	baculoviral IAP repeat-containing 5 (survivin)	NM_001168	17q25
		NM_198053
CD3Z	CD3Z antigen, zeta polypeptide (TiT3 complex)	NM_000734	1q22-q23
TNFRSF10A	tumor necrosis factor receptor superfamily, member 10a	NM_003844	8p21
CD4	CD4 antigen (p55)	NM 000616	12pter-p12

Table 3 : List of antibodies
Antibody	Common name	Clone	Isotype	Fluoroch rom	specie	Manufacturer
CCR5	CCR5	45531	IgG2b	FITC	mouse	R&D systems
CCR7	CCR7	3D12	IgG2a	PE	rat	BD pharmingen
CD103	Integrin alpha E	Ber-ATC8	lgG1	PE	mouse	BD pharmingen
CD119	IFN-gamma-R1	BB1E2	IgG2a	FITC	mouse	serotec
CD120a	TNFR1	H398	IgG2a	PE	mouse	serotec
CD120b	TNFR2	MR2-1	IgG1	PE	mouse	serotec
CD122	IL-2R-beta	MIK-beta1	IgG2a	FITC	mouse	serotec
CD127	IL-7R-alpha	R34.34	IgG1	PE	mouse	beckman coulter
CD134	OX40L-R	ACT35	IgG1	FITC	mouse	BD pharmingen
CD14	CD14	M5E2	IgG2a	APC	mouse	BD pharmingen
CD152	CTLA-4	BNI3	IgG2a	APC	mouse	BD pharmingen
CD154	CD40L	TRAP1	IgG1	FITC	mouse	BD pharmingen
CD178	FasL	NOK1	IgG1	-	mouse	BD pharmingen
CD183	CXCR3	1C6/CXCR3	IgG1	APC	mouse	BD pharmingen
CD184	CXCR4	12G5	IgG2a	APC	mouse	BD pharmingen
CD19	CD19	HIB19	IgG1	FITC	mouse	BD pharmingen
CD1a	CD1a	HI149	IgG1	FITC	mouse	BD pharmingen
CD210	IL-10R-alpha	3F9	IgG2a	PE	rat	BD pharmingen
CD25	IL-2R-alpha	M-A251	IgG1	APC	mouse	BD pharmingen
CD26	dipeptidyl- peptidase IV	M-A261	IgG1	PE	mouse	BD pharmingen
CD27	CD27	M-T271	IgG1	PE	mouse	BD pharmingen
CD28	CD28	CD28.2	IgG1	APC	mouse	BD pharmingen
CD3	CD3ε	UCHT1	IgG1	FITC	mouse	BD pharmingen
CD3	CD3ε	UCHT1	IgG1	CyCr	mouse	BD pharmingen
CD3	CD3ε	S4.1	IgG2a	PE-Cy5	mouse	serotec
CD32	FcγRII	AT10	IgG1	FITC	mouse	serotec
CD4	CD4	RPA-T4	IgG1	FITC	mouse	BD pharmingen
CD4	CD4	RPA-T4	IgG1	PE	mouse	BD pharmingen
CD44	CD44	G44-26	IgG2b	APC	mouse	BD pharmingen
CD45	CD45	HI30	IgG1	CyCr	mouse	BD pharmingen
CD45Ra	CD45Ra	HI100	IgG2b	FITC	mouse	BD pharmingen
CD45Ro	CD45Ro	UCHTL1	IgG2a	APC	mouse	BD pharmingen
CD47	CD47	B6H12	IgG1	PE	mouse	BD pharmingen
CD49d	VLA-4	9F10	IgG1	PE	mouse	BD pharmingen
CD5	CD5	UCHT2	IgG1	PE	mouse	BD pharmingen
CD54	ICAM-1	HA58	IgG1	PE	mouse	BD pharmingen
CD56	CD56	B159	IgG1	PE	mouse	BD pharmingen
CD62L	L-selectin	Dreg56	IgG1	FITC	mouse	BD pharmingen
CD69	CD69	FN50	IgG1	APC	mouse	BD pharmingen
CD7	CD7	M-T701	IgG1	FITC	mouse	BD pharmingen
CD8	CD8	RPA-T8	IgG1	APC	mouse	BD pharmingen
CD8	CD8	HIT8a	IgG1	PE	mouse	BD pharmingen
CD80	B7.1	L307.4	IgG1	PE	mouse	BD pharmingen
CD83	CD83	HB15e	IgG1	PE	mouse	BD pharmingen
CD86	B7.2	FUN-1	IgG1	FITC	mouse	BD pharmingen
CD95	Fas	DX2	IgG1	APC	mouse	BD pharmingen
CD97	CD97	VIM3b	IgG1	FITC	mouse	BD pharmingen
CD98	CD98	UM7F8	IgG1	FITC	mouse	BD pharmingen
CXCR6	CXCR6	56811	IgG2b	PE	mouse	R&D systems
GITR	GITR	polyclonal	IgG	-	goat	R&D systems
HLA-DR	HLA-DR	G46.6(L243)	IgG2a	FITC	mouse	BD pharmingen
ICOS	ICOS	C394.4A	IgG	PE	mouse	clinisciences
IFNγRII	IFNγRII	polyclonal	IgG	-	goat	R&D systems
IL-18Rα	IL-18Rα	70625	IgG1	PE	mouse	R&D systems
KIR-NKAT2	KIR-NKAT2	DX27	IgG2a	FITC	mouse	BD pharmingen
PD1	PD1	J116	IgG1	PE	mouse	clinisciences
Streptavidin	Streptavidin	-	-	APC	-	BD pharmingen
TCR αβ	TCRαβ	T10B9.A1-31	IgM	FITC	mouse	BD pharmingen
TGFRII	TGFRII	25508	IgG1	FITC	mouse	R&D systems

table 4: Correlation analysis
Genes	Correlation	95% CI	P value
CD8A - TBX21	0,902	(0.848 / 0.938 )	<0.0001
CD3Z - CD8A	0,797	(0.694 / 0.868)	<0.0001
CD3Z - TBX21	0,784	(0.676 / 0.859)	<0.0001
B7H3 - TGFB1	0,760	(0.643 / 0.843)	<0.0001
IFNG - TBX21	0,759	(0.635 / 0.844) .	<0.0001
CD4 - CD8A	0,738	(0.612 / 0.828)	<0.0001
CD8A - IFNG	0,728	(0.592 / 0.823)	<0.0001
CD4 - TBX21	0,727	(0.597 / 0.820)	<0.0001
CD3Z - CD4	0,719	(0.586 / 0.815)	<0.0001
CD4 - TGFB1	0,678	(0.531 / 0.786)	<0.0001
CD8A - GNLY	0,671	(0.522 / 0.781)	<0.0001
IFNG - IRF1	0,664	(0.505 / 0.779)	<0.0001
GNLY - IFNG	0,663	(0.505 / 0.779)	<0.0001
IRF1 - TBX21	0,656	(0.502 / 0.770)	<0.0001
IL8 - PTGS2	0,643	(0.485 / 0.761)	<0.0001
GNLY - TBX21	0,627	(0.464 / 0.749)	<0.0001
CD3Z - IRF1	0,617	(0.451 / 0.742)	<0.0001
CD8A - IRF1	0,617	(0.451 / 0.742)	<0.0001
CD3Z - GNLY	0,613	(0.446 / 0.739)	<0.0001
CD3Z - IFNG	0,605	(0.428 / 0.737)	<0.0001
GZMB - IFNG	0,604	(0.422 / 0.739)	<0.0001
GNLY - IRF1	0,597	(0.425 / 0.727)	<0.0001
IL10 - TGFB1	0,596	(0.424 / 0.726)	<0.0001
CDA - IL10	0,586	(0.411 / 0.719)	<0.0001
CD4 - IL10	0,583	(0.408 / 0.717)	<0.0001
CD8A - GZMB	0,574	(0.392 / 0.713)	<0.0001
GZMB - TBX21	0,548	(0.359 / 0.693)	<0.0001
CD3Z - GZMB	0,538	(0.347 / 0.687)	<0.0001
CD4 - IRF1	0,520	(0.330 / 0.670)	<0.0001
GNLY - GZMB	0,520	(0.324 / 0.673)	<0.0001
B7H3 - IL10	0,517	(0.326 / 0.668)	<0.0001
CD4 - GZMB	0,507	(0.309 / 0.663)	<0.0001
GZMB - IRF1	0,504	(0.305 / 0.661)	<0.0001
IL10 - TBX21	0,494	(0.297 / 0.650)	<0.0001
CD4 - IFNG	0,493	(0.289 / 0.655)	<0.0001
B7H3 - CD4	0,475	(0.275 / 0.636)	<0.0001
CD8A - TGFB1	0,466	(0.264 / 0.628)	<0.0001
CD3Z - IL10	0,459	(0.255 / 0.623)	<0.0001
CD4 - GNLY	0,454	(0.250 / 0.619)	<0.0001
TBX21 - TGFB1	0,433	(0.226 / 0.603)	0.0001
GNLY - IL10	0,413	(0.202 / 0.587)	0.0002
CD3Z - TGFB1	0,398	(0.185 / 0.575)	0.0004
IFNG - IL10	0,390	(0.168 / 0.575)	0.0009
B7H3 - VEGF	0,371	(0.155 / 0.554)	0.0011
B7H3 - IL8	0,370	(0.152 / 0.553)	0.0012
CEACAM1 - IRF1	0,359	(0.140 / 0.544)	0.0017
IL10 - IRF1	0,355	(0.136 / 0.541)	0.0019
IRF1 - VEGF	0,351	(0.131 / 0.538)	0.0022
B7H3 - MMP7	0,335	(0.112 / 0.526)	0.0038
B7H3 - PTGS2	0,333	(0.112 / 0.523)	0.0037
IRF1 - TGFB1	0,333	(0.111 / 0.523)	0.0038
IL10 - PTGS2	0,325	(0.103 / 0.517)	0.0047
GZMB - IL10	0,320	(0.092 / 0.517)	0.0066
CD4 - VEGF	0,316	(0.093 / 0.509)	0.0062
GZMB - TGFB1	0,306	(0.076 / 0.504)	0.0097
IL8 - MMP7	0,295	(0.068 / 0.493)	0.0116
TBX21 - VEGF	0,294	(0.069 / 0.491)	0.0113
CEACAM1 - VEGF	0,292	(0.066 / 0.489)	0.0119
TGFB1 - VEGF	0,290	(0.065 / 0.488)	0.0124
BIRC5 - IRF1	0,265	(0.037 / 0.466)	0.0234
GNLY - TGFB1	0,257	(0.029 / 0.460)	0.0278
PTGS2 - TGFB1	0,257	(0.028 / 0.459)	0.0281
MMP7 - VEGF	0,251	(0.020 / 0.456)	0.0332
IFNG - TGFB1	0,239	(0.001 / 0.452)	0.0492
IRF1 - TNFRSF10A	0,238	(0.009 / 0.444)	0.042
BIRC5 - PTGS2	0,224	(-0.007 / 0.431)	0.0571
IL8 - TGFB1	0,223	(-0.007 / 0.431)	0.0578
B7H3 - IRF1	0,222	(-0.009 / 0.430)	0.059
MMP7 - TGFB1	0,221	(-0.012 / 0.430)	0.0622
B7H3 - CD8A	0,216	(-0.015 / 0.425)	0.0664
GZMB - VEGF	0,209	(-0.028 / 0.423)	0.0829
CD3Z - VEGF	0,207	(-0.024 / 0.418)	0.0784
IFNG - IL8	0,206	(-0.034 / 0.424)	0.0922
CD3Z - CEACAM1	0,204	(-0.027 / 0.415)	0.0836
CD8A - VEGF	0,203	(-0.028 / 0.414)	0.0846
IL10 - IL8	0,196	(-0.036 / 0.408)	0.0967
BIRC5 - IFNG	0,195	(-0.045 / 0.414)	0.111
GZMB - IL8	0,194	(-0.043 / 0.410)	0.1087
B7H3 - TBX21	0,191	(-0.041 / 0.403)	0.1056
B7H3 - CD3Z	0,188	(-0.044 / 0.401)	0.1109
CD4 - MMP7	0,181	(-0.052 / 0.397)	0.1274
CEACAM1 - TBX21	0,174	(-0.059 / 0.388)	0.1416
GNLY - PTGS2	0,173	(-0.059 / 0.388)	0.1435
MMP7 - PTGS2	0,162	(-0.073 / 0.379)	0.1748
BIRC5 - GZMB	0,161	(-0.077 / 0.381)	0.1842
B7H3 - GZMB	0,160	(-0.078 / 0.381)	0.1862
CD4 - TNFRSF10A	0,160	(-0.072 / 0.377)	0.176
IFNG - TNFRSF10A	0,156	(-0.086 / 0.380)	0.2048
GNLY - TNFRSF10A	0,153	(-0.079 / 0.370)	0.1957
TBX21 - TNFRSF10A	0,147	(-0.086 / 0.365)	0.2157
BIRC5 - IL8	0,145	(-0.088 / 0.363)	0.2225
TNFRSF10A - VEGF	0,136	(-0.097 / 0.355)	0.2518
B7H3 - TNFRSF10A	0,135	(-0.098 / 0.355)	0.2541
CD8A - TNFRSF10A	0,134	(-0.099 / 0.354)	0.2577
GZMB - PTGS2	0,134	(-0.104 / 0.358)	0.2702
CEACAM1 - TNFRSF10A	0,133	(-0.100 / 0.352)	0.2641
B7H3 - IFNG	0,126	(-0.116 / 0.353)	0.3088
IFNG - VEGF	0,123	(-0.119 / 0.351)	0.3177
CD3Z - TNFRSF10A	0,117	(-0.117 / 0.338)	0.3269
BIRC5 - CEACAM1	0,109	(-0.124 / 0.331)	0.3597
GNLY - IL8	0,106	(-0.128 / 0.328)	0.3754
IFNG - PTGS2	0,106	(-0.136 / 0.336)	0.3903
GZMB - TNFRSF10A	0,104	(-0.135 / 0.331)	0.3942
CEACAM1 - IFNG	0,093	(-0.148 / 0.325)	0.4506
B7H3 - GNLY	0,090	(-0.143 / 0.313)	0.4514
BIRC5 - GNLY	0,088	(-0.145 / 0.311)	0.4628
CEACAM1 - GZMB	0,087	(-0.151 / 0.316)	0.4736
CEACAM1 - GNLY	0,082	(-0.151 / 0.306)	0.4911
IL10 - MMP7	0,081	(-0.153 / 0.307)	0.499
IL8 - VEGF	0,078	(-0.155 / 0.303)	0.5132
BIRC5 - MMP7	0,077	(-0.157 / 0.304)	0.5192
CD8A-CEACAM1	0,076	(-0.157 / 0.301)	0.5232
TGFB1 - TNFRSF10A	0,071	(-0.162 / 0.296)	0.5538
BIRC5 - VEGF	0,065	(-0.168 / 0.291)	0.5855
IRF1 - PTGS2	0,064	(-0.169 / 0.289)	0.594
IRF1 - MMP7	0,063	(-0.171 / 0.290)	0.6012
PTGS2 - VEGF	0,063	(-0.170 / 0.289)	0.5995
CEACAM1 - MMP7	0,035	(-0.199 / 0.264)	0.7742
IL10 - TNFRSF10A	0,032	(-0.199 / 0.261)	0.786
IL8 - IRF1	0,021	(-0.211 / 0.249)	0.8633
CD4 - CEACAM1	0,014	(-0.217 / 0.243)	0.9088
BIRC5 - TBX21	0,013	(-0.218 / 0.242)	0.9124
IFNG - MMP7	0,009	(-0.231 / 0.249)	0.9402
CD3Z - MMP7	0,005	(-0.227 / 0.236)	0.968
CEACAM1 - PTGS2	-0,001	(-0.231 / 0.229)	0.9923
IL10 - VEGF	-0,004	(-0.234 / 0.226)	0.9721
CD8A - PTGS2	-0,008	(-0.238 / 0.222)	0.9448
GZMB - MMP7	-0,008	(-0.244 / 0.229)	0.947
IL8 - TNFRSF10A	-0,017	(-0.246 / 0.214)	0.8892
GNLY - VEGF	-0,023	(-0.252 / 0.208)	0.8484
PTGS2 - TBX21	-0,036	(-0.264 / 0.196)	0.7631
MMP7 - TBX21	-0,049	(-0.277 / 0.185)	0.6844
BIRC5 - CD8A	-0,051	(-0.278 / 0.181)	0.6675
CD3Z - PTGS2	-0,051	(-0.278 / 0.181)	0.6683
BIRC5 - CD3Z	-0,054	(-0.280 / 0.179)	0.6528
B7H3 - CEACAM1	-0,063	(-0.289 / 0.169)	0.5972
PTGS2 - TNFRSF10A	-0,066	(-0.292 / 0.166)	0.5782
CD8A - MMP7	-0,086	(-0.311 / 0.149)	0.4739
B7H3 - BIRC5	-0,095	(-0.318 / 0.138)	0.4236
CD4 - IL8	-0,101	(-0.323 / 0.133)	0.3987
CEACAM1 - IL8	-0,101	(-0.323 / 0.132)	0.3979
CD4 - PTGS2	-0,111	(-0.333 / 0.122)	0.3494
CEACAM1 - IL10	-0,111	(-0.333 / 0.122)	0.3495
IL8 - TBX21	-0,131	(-0.350 / 0.102)	0.2714
BIRC5 - IL10	-0,134	(-0.353 / 0.099)	0.2583
CD8A - IL8	-0,163	(-0.378 / 0.070)	0.1701
MMP7 - TNFRSF10A	-0,217	(-0.427 / 0.015)	0.0668
BIRC5 - TGFB1	-0,218	(-0.426 / 0.013)	0.0643
BIRC5 - CD4	-0,231	(-0.438 / -0.001)	0.0489
CEACAM1 - TGFB1	-0,239	(-0.445 / -0.010)	0.0413
GNLY - MMP7	-0,241	(-0.448 / -0.010)	0.0408
BIRC5 - TNFRSF10A	-0,243	(-0.448 / -0.014)	0.0378
CD3Z - IL8	-0,258	(-0.461 / -0.030)	0.0272

Table 5
Table 5		Disease Free Survival (DFS)
N. of patients	Rate at 2 yr %	Rate at 4 yr %	Rate at 5 yr %	Median months	P value
GZM-CT						1.66 E-06
Hi	163	57.66	46.62	41.71	36.5
Lo	191	35.93	26.04	21.87	12.4
GZM-IM						9.42 E-07
Hi	175	56.00	44.57	38.28	36.6
Lo	129	38.76	31.00	26.36	12.9
CD45RO-CT						1.43 E-08
Hi	294	51.70	40.81	36.05	27.4
Lo	67	20.58	11.76	8.82	2.4
CD45RO-IM						2.16 E-06
Hi	190	56.31	46.84	42.10.	42.0
Lo	178	35.19	24.58	20.11	12.4
CD8-CT						3.68 E-08
Hi	227	54.18	43.17	38.32	31.1
Lo	132	27.81	20.30	16.54	5.9
CD8-IM						1.53 E-04
Hi	129	60.93	50.78	45.31	49.2
Lo	185	40.64	29.41	24.59	16.6
CD3-CT						3.90 E-08
Hi	192	60.41	48.95	43.75	45.9
Lo	165	28.91	19.87	16.26	5.9
CD3-IM						3.37 E-08
Hi	178	59.77	49.72	44.69	47.8
Lo	175	35.42	24.57	20,00	12.9
GZM-CT/IM						3.67 E-07
HiHi	95	62.74	51.96	46.07	51.4
LoLo	80	39.43	29.57	25.35	12.9
CD45RO-CT/IM						4.57 E-10
HiHi	151	60.26	52.31	47.68	51.6
LoLo	41	16.66	11.90	9.52	1.8
CD8-CT/IM						4.61 E-08
HiHi	96	65.62	55.20	50,00	59.2
LoLo	93	30.85	21.27	17.02	5.9
CD3-CT/IM						5.20 E-11
HiHi	109	69.72	61.46	55.04	66.2
LoLo	93	27.95	19.35	13.97	5.9

Table 6
Table 6		Overall Survival (OS)
N. of patients	Rate at 2 yr %	Rate at 4 yr %	Rate at 5 yr %	Median months	P value
GZM-CT						8.18 E-07
Hi	163	62.58	50.31	43.56	50.2
Lo	191	48.17	32.46	25.13	21.2
GZM-IM						1.27 E-02
Hi	175	61.14	48.57	39.43	45.3
Lo	129	55.04	37.21	29.46	29.1
CD45RO-CT						3.14 E-09
Hi	294	57.82	45.92	38.78	34.9
Lo	67	37.31	16.42	11.94	16.4
CD45RO-IM						7.68 E-04
Hi	190	62.63	50.53	44.21	49.2
Lo	178	46.63	30.90	23.03	19.8
CD8-CT						2.66 E-07
Hi	227	59.47	48.02	40.53	42.5
Lo	132	43.94	25.76	19.70	18.7
CD8-IM						1.22 E-03
Hi	129	65.63	53.91	46.88	54.8
Lo	185	54.84	37.10	28.50	29.6
CD3-CT						7.86 E-08
Hi	192	65.63	55.21	47.40	57.8
Lo	165	42.42	24.24	18.18	18.6
CD3-IM						9.08 E-05
Hi	178	64.04	52.25	46.07	52.6
Lo	175	48.57	32.57	24,00	21.4
GZM-CT/IM						1.50 E-03
HiHi	95	68.42	55.79	47.37	58.3
LoLo	80	58.75	37.50	28.75	32.0
CD45RO-CT/IM						4.12 E-07
HiHi	151	64.24	54.97	49.67	59.6
LoLo	41	39.02	14.63	12.20	17.7
CD8-CT/IM						1.21 E-06
HiHi	96	69.79	59.38	52.08	61.2
LoLo	93	49.46	29.03	21.51	22.3
CD3-CT/IM						5.07 E-08
HiHi	109	72.48	64.22	55.96	63.9
LoLo	93	45.16	25.81	16.13	19.3

Table 7: Multivariate proportional hazard analysis for DFS
Variable*	Hazard ratio	95% CI	P
T-stage	1.780	(1.348-2.362)	5.2 E-05
N-stage	2.130	(1.481-3.060)	4.5 E-05
Differentiation	1.110	(0.777-1.584)	5.7 E-01
CD3 CT /CD3 IM patterns	0.570	(0.450-0.721)	2.8 E-06
Variable*	Hazard ratio	95% CI	P
T-stage	1.700	(1.275-2.268)	3.1 E-04
N-stage	2.117	(1.449-3.093)	1.1 E-04
Differentiation	0.969	(0.676-1.389)	8.6 E-01
CD8 CT /CD8 IM patterns	0.614	(0.480-0.786)	1.1 E-04
Variable*	Hazard ratio	95% CI	P
T-stage	1.880	(1.441-2.452)	3.3 E-06
N-stage	2.298	(1.599-3.301)	6.8 E-06
Differentiation	1.035	(0.736-1.457)	8.4 E-01
CD45RO CT /CD45RO IM patterns	0.564	(0.439-0.723)	6.2 E-06
Variable*	Hazard ratio	95% CI	P
T-stage	1.777	(1.334-2.37)	8.5 E-05
N-stage	2.449	(1.651- 3.63)	8.3 E-06
Differentiation	1.049	(0.707-1.56)	8.1 E-01
GZMB CT /GZMB IM patterns	0.591	(0.459-0.76)	4.3 E-05
* M stratified

Table 7: Multivariate proportional hazard analysis for OS
Variable	Hazard ratio	95% CI	P
T-stage	1.335	(1.052-1.693)	1.7 E-02
N-stage	1.657	(2.989-6.595)	3.6 E-03
M-stage	4.440	(1.179-2.328)	1.5 E-13
Differentiation	1.058	(0.748-1.496)	7.5 E-01
CD3 CT/ CD3 IM patterns	0.726	(0.587-0.897)	3.0 E-03
Variable	Hazard ratio	95% CI	P
T-stage	1.376	(1.070-1.769)	1.3 E-02
N-stage	1.575	(1.100-2.254)	1.3 E-02
M-stage	4.467	(2.966-6.729)	8.0 E-13
Differentiation	0.966	(0.679-1.375)	8.5 E-01
CD8 CT /CD8 IM patterns	0.712	(0.571-0.888)	2.5 E-03
Variable	Hazard ratio	95% CI	P
T-stage	1.396	(1.114-1.750)	3.7 E-03
N-stage	1.684	(1.204-2.355)	2.3 E-03
M-stage	4.160	(2.805-6.170)	1.4 E-12
Differentiation	0.935	(0.677-1.292)	6.9 E-01
CD45RO CT /CD45RO IM patterns	0.703	(0.558-0.885)	2.8 E-03
Variable	Hazard ratio	95% CI	P
T-stage	1.360	(1.071-1.73)	1.2 E-02
N-stage	1.710	(1.188-2.46)	3.9 E-03
M-stage	4.392	(2.866-6.73)	1.1 E-11
Differentiation	1.094	(0.752-1.59)	6.4 E-01
GZMB CT /GZMB IM patterns	0.905	(0.722-1.14)	3.9 E-01